代码更新

Ortho/OrthoAlg.py

@@ -8,6 +8,8 @@
 @Date ：2021/8/14
 @Version ：1.0.0
 """
+import glob
+
 # from re import I, T, X, match
 # from types import DynamicClassAttribute
 # from numpy.core.einsumfunc import _parse_possible_contraction
@@ -56,7 +58,7 @@ class ScatteringAlg:
         pass
 
     @staticmethod
-    def sar_backscattering_coef(in_sar_tif, meta_file_path, out_sar_tif, replece_VV = False, is_DB = True):
+    def sar_backscattering_coef(in_sar_tif, meta_file_path, out_sar_tif, inc_xml_path, replece_VV = False, is_DB = True):
 
         # 读取原始SAR影像
         proj, geotrans, in_data = ImageHandler.read_img(in_sar_tif)
@@ -103,6 +105,9 @@ class ScatteringAlg:
         coef_arr[where_9999_1] = -9999
         ## 输出的SAR后向散射系数产品
         # ImageHandler.write_img(out_sar_tif, proj, geotrans, coef_arr, 0)
+        _, _, inc_arr = ImageHandler().read_img(inc_xml_path)
+        rad_arr = np.radians(inc_arr)
+        sin_arr = np.sin(rad_arr)
 
         tif_array = np.power(10.0, coef_arr / 10.0)  # dB --> 线性值  后向散射系数
 
@@ -110,6 +115,7 @@ class ScatteringAlg:
         tif_array[np.isinf(tif_array)] = 0
         tif_array[where_9999_0] = 0
         tif_array[where_9999_1] = 0
+        tif_array = tif_array / sin_arr
 
         ImageHandler.write_img(out_sar_tif, proj, geotrans, tif_array, 0)
         return True
@@ -122,6 +128,16 @@ class ScatteringAlg:
         # db_arr[np.isinf(db_arr)] = -9999
         ImageHandler.write_img(db_path, proj, geotrans, db_arr, -9999)
 
+    @staticmethod
+    def lin_to_db_top(lin_path, db_path):
+        proj, geotrans, in_data = ImageHandler.read_img(lin_path)
+        db_arr = 10 * np.log10(in_data)
+        db_arr[np.isnan(db_arr)] = 0
+        db_arr[np.isinf(db_arr)] = 0
+        # db_arr[np.isnan(db_arr)] = -9999
+        # db_arr[np.isinf(db_arr)] = -9999
+        ImageHandler.write_img(db_path, proj, geotrans, db_arr, '0')
+
     @staticmethod
     def sar_backscattering_coef_RPC(in_sar_tif, meta_file_path, out_sar_tif, replece_VV=False, is_DB=True):
 
@@ -174,6 +190,17 @@ class ScatteringAlg:
 
         return True
 
+    @staticmethod
+    def get_incidence_xml_paths(file_dir, name):
+        meta_xml_paths = []
+        if os.path.exists(file_dir + name + '\\'):
+            meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.incidence.xml')))
+        else:
+            meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.incidence.xml')))
+        if meta_xml_paths is None or meta_xml_paths == []:
+            raise Exception('there is not .incidence.xml in path: ', file_dir + '\\')
+        return meta_xml_paths
+
 
 
 def FindInfomationFromJson(HeaderFile_dom_json, node_path_list):
@@ -1086,6 +1113,38 @@ class DEMProcess(object):
         # gdal.CloseDir(out_DEM)
         return out_DEM
 
+    @staticmethod
+    def tif_merged(in_tif_path, temp_dir, out_tif_path):
+        '''
+        DEM重采样函数，默认坐标系为WGS84
+        agrs:
+            in_dem_path: 输入的DEM文件夹路径
+            meta_file_path: 输入的xml元文件路径
+            out_dem_path: 输出的DEM文件夹路径
+        '''
+        # 读取文件夹中所有的DEM
+        bsMap_file_paths = [os.path.join(in_tif_path, dem_name) for dem_name in os.listdir(in_tif_path) if
+                            dem_name.find(".tif") >= 0 and dem_name.find(".tif.") == -1]
+        spatialreference = osr.SpatialReference()
+        spatialreference.SetWellKnownGeogCS("WGS84")  # 设置地理坐标，单位为度 degree # 设置投影坐标，单位为度 degree
+        spatialproj = spatialreference.ExportToWkt()  # 导出投影结果
+
+        # # 将DEM拼接成一张大图
+        baseName = os.path.splitext(os.path.basename(bsMap_file_paths[0]))[0]
+        mergeFile = gdal.BuildVRT(os.path.join(temp_dir, f"{baseName}_VRT.tif"), bsMap_file_paths)
+        out_DEM = os.path.join(out_tif_path, f"{baseName.split('_Strip')[0]}-ortho.tif")
+        gdal.Warp(out_DEM,
+                  mergeFile,
+                  format="GTiff",
+                  dstSRS=spatialproj,
+                  srcNodata=0,
+                  dstNodata=-9999,
+                  outputType=gdal.GDT_Float32,
+                  resampleAlg='average')
+        time.sleep(3)
+        # gdal.CloseDir(out_DEM)
+        return out_DEM
+
     @staticmethod
     def dem_resampled(in_dem_path,out_dem_path,samling_f):
         in_dem=gdal.Open(in_dem_path,gdalconst.GA_ReadOnly)
@@ -1280,6 +1339,144 @@ class Orthorectification(object):
         return HeaderInformation_json
         pass
 
+    def ParseHearderFile_top(self, HeaderFilePath_str, pol_id):
+        """
+        解析头文件，返回计算所需要的必要信息。
+        args:
+            HeaderFilePath_str: 头文件地址
+        return:
+            HeaderFileInfomation_json: 头文件信息包
+        """
+
+        with open(HeaderFilePath_str, 'r', encoding='utf-8') as fp:
+            HeaderFile_dom_str = fp.read()
+        HeaderFile_dom = xmltodict.parse(HeaderFile_dom_str)  # 将XML转成json文本
+        HeaderFile_dom_json = json.loads(json.dumps(HeaderFile_dom))  # 将json字符串，转成json对象（对应于python中的dict）
+        # 获取头文件
+        HeaderInformation_json = {}
+        # 解析轨道节点，假定是GPS节点
+        HeaderInformation_json['GPS'] = []
+        # GPS 解析信息
+        GPSNode_Path = self.config['GPS']['GPSNode_Path']
+        GPSNodeNames_list = self.config['GPS']['NodeInfomation_Name']
+        GPSTime_Format = self.config['GPS']['Time_format']
+        GPSPoints = FindInfomationFromJson(HeaderFile_dom_json, GPSNode_Path)
+
+        for GPSPoint in GPSPoints:
+            GPSPoint = [
+                datetime.datetime.strptime(GPSPoint[GPSNodeNames_list[0]], GPSTime_Format).timestamp(),  # TimeStamp
+                float(GPSPoint[GPSNodeNames_list[1]]),  # Xp
+                float(GPSPoint[GPSNodeNames_list[2]]),  # Yp
+                float(GPSPoint[GPSNodeNames_list[3]]),  # Zp
+                float(GPSPoint[GPSNodeNames_list[4]]),  # Vx
+                float(GPSPoint[GPSNodeNames_list[5]]),  # Vy
+                float(GPSPoint[GPSNodeNames_list[6]])]  # VZ
+            HeaderInformation_json['GPS'].append(GPSPoint)
+
+        # 提取成像相关信息
+        HeaderInformation_json['ImageInformation'] = {}
+        # 1、开始成像时间
+        HeaderInformation_json['ImageInformation']['StartTime'] = datetime.datetime.strptime(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['StartImageTime']['NodePath']),
+            self.config['imageinfo']['StartImageTime']['Format']
+        ).timestamp()
+        # 2、结束成像时间
+        HeaderInformation_json['ImageInformation']['EndTime'] = datetime.datetime.strptime(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['EndImageTime']['NodePath']),
+            self.config['imageinfo']['StartImageTime']['Format']
+        ).timestamp()
+        # 3、影像的宽高
+        temp = FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['ImageHeight']['NodePath'])
+        HeaderInformation_json['ImageInformation']['height'] = int(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['ImageHeight']['NodePath']).split(',')[
+                pol_id])
+        HeaderInformation_json['ImageInformation']['width'] = int(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['ImageWidth']['NodePath']).split(',')[
+                pol_id])
+        # 4、影像的近斜距
+        HeaderInformation_json['ImageInformation']['NearRange'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['NearRange']['NodePath']).split(',')[
+                pol_id])
+        # 5、入射角
+        HeaderInformation_json['ImageInformation']['incidenceAngle'] = {
+            'NearRange': float(FindInfomationFromJson(HeaderFile_dom_json,
+                                                      self.config['imageinfo']['incidenceAngle']['NearRange'][
+                                                          'NodePath'])),
+            'FarRange': float(FindInfomationFromJson(HeaderFile_dom_json,
+                                                     self.config['imageinfo']['incidenceAngle']['FarRange'][
+                                                         'NodePath']))
+        }
+        # 6、成像时刻影像带宽
+        bandWidth_list = self.config['sensor']['bandWidth']['NodePath']
+        bandWidth_list.insert(4, pol_id)
+        HeaderInformation_json['ImageInformation']['bandWidth'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, bandWidth_list))
+        # 7、多普勒质心常数
+        DopplerCentroidCoefficients_list = FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo'][
+            'DopplerCentroidCoefficients']['NodePath'])
+        HeaderInformation_json['ImageInformation']['DopplerCentroidCoefficients'] = [
+            float(DopplerCentroidCoefficients_list[
+                      self.config['imageinfo']['DopplerCentroidCoefficients']['DopplerCentroidCoefficients_Name'][0]]),
+            float(DopplerCentroidCoefficients_list[
+                      self.config['imageinfo']['DopplerCentroidCoefficients']['DopplerCentroidCoefficients_Name'][1]]),
+            float(DopplerCentroidCoefficients_list[
+                      self.config['imageinfo']['DopplerCentroidCoefficients']['DopplerCentroidCoefficients_Name'][2]]),
+            float(DopplerCentroidCoefficients_list[
+                      self.config['imageinfo']['DopplerCentroidCoefficients']['DopplerCentroidCoefficients_Name'][3]]),
+            float(DopplerCentroidCoefficients_list[
+                      self.config['imageinfo']['DopplerCentroidCoefficients']['DopplerCentroidCoefficients_Name'][4]]),
+        ]
+        # 8、波长
+        HeaderInformation_json['ImageInformation']['lambda'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['sensor']['lambda']['NodePath']))
+        # 9、中心像元对应的中心经纬度
+        CenterPoint = FindInfomationFromJson(HeaderFile_dom_json,
+                                             self.config['imageinfo']['CenterImagePositon']['NodePath'])
+        HeaderInformation_json['ImageInformation']['centerPosition'] = [
+            float(CenterPoint[self.config['imageinfo']['CenterImagePositon']['Value'][0]]),  # 纬度
+            float(CenterPoint[self.config['imageinfo']['CenterImagePositon']['Value'][1]]),  # 经度
+        ]
+        # 10、多普勒参数参考时间
+        HeaderInformation_json['ImageInformation']['DopplerParametersReferenceTime'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json,
+                                   self.config['imageinfo']['DopplerParametersReferenceTime']['NodePath']))
+        # 11、参考斜距
+        HeaderInformation_json['ImageInformation']['refRange'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['ReferenceRange']['NodePath']).split(
+                ',')[pol_id])
+        # 12、PRF
+        HeaderInformation_json['PRF'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['sensor']['PRF']['NodePath']).split(',')[pol_id])
+        HeaderInformation_json['Fs'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['sensor']['Fs']['NodePath']).split(',')[pol_id])
+        # 13、中心时间
+        HeaderInformation_json['ImageInformation']['CenterTime'] = datetime.datetime.strptime(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['CenterImageTime']['NodePath']),
+            self.config['imageinfo']['CenterImageTime']['Format']
+        ).timestamp()
+        # 14. 影像的空间间隔分辨率
+        HeaderInformation_json['ImageInformation']['ImageWidthSpace'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['ImageWidthSpace']['NodePath']).split(
+                ',')[pol_id])
+
+        HeaderInformation_json['ImageInformation']['ImageHeightSpace'] = float(
+            FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['ImageHeightSpace']['NodePath']).split(
+                ',')[pol_id])
+
+        # 部分需要解析
+        self.lamda = HeaderInformation_json['ImageInformation']['lambda']
+        self.PRF = HeaderInformation_json['PRF']
+        self.imgwidth = HeaderInformation_json['ImageInformation']['width']
+        self.imgheight = HeaderInformation_json['ImageInformation']['height']
+        self.imgstarttime = HeaderInformation_json['ImageInformation']['StartTime']
+        self.widthspace = HeaderInformation_json['ImageInformation']['ImageWidthSpace']
+        self.heightspace = HeaderInformation_json['ImageInformation']['ImageHeightSpace']
+        self.refrange = HeaderInformation_json['ImageInformation']['refRange']
+        self.nearrange = HeaderInformation_json['ImageInformation']['NearRange']
+        self.Fs = HeaderInformation_json['Fs'] * 1e6  # Mhz
+        return HeaderInformation_json
+        pass
+
     def LLA_to_XYZ(self, latitude, longitude, altitude):
         """
         经纬度转大地坐标系
@@ -1617,6 +1814,45 @@ class IndirectOrthorectification(Orthorectification):
         self.PrepareConvertSystem()
         self.outParameterText(os.path.join(workspace_dir,"orth_para.txt"))
 
+
+    def IndirectOrthorectification_top(self, xml_path, tif_path, workspace_dir):
+        """
+        正射校正组件
+        正射校正整体可以分为两个步骤：
+        Step 1：计算出栅格坐标对应的真实的大地坐标系坐标
+        Step 2：根据大地坐标系将影像重采样到大地坐标系空间中
+        args:
+            FilePath_str：影像所在文件夹
+            dem_resampled_path：重采样后的DEM路径
+            lut_tif_path：输出的r,c,ti数据影像
+            work_temp_path: 输出的临时文件地址路径，方便存放计算临时文件
+        """
+        # 分离需要校正对象
+        pol_id = os.path.splitext(os.path.basename(tif_path))[0].split('_')[-1]
+        # 解析头文件
+        self.header_info = self.ParseHearderFile_top(xml_path, int(pol_id))
+        # 构建轨道模型
+        self.SatelliteOrbitModel = ReconstructionSatelliteOrbit(self.header_info['GPS'],
+                                                                starttime=self.header_info['ImageInformation'][
+                                                                    'CenterTime'])  # 构建卫星轨道模型,取第0个节点的时间
+        # 获取所有轨道节点时间
+        gpslist = np.array(self.header_info['GPS']).reshape(-1, 7)
+        # 验证轨道模型
+        tempp = gpslist[:, 0].reshape(-1)
+        statepoints = self.SatelliteOrbitModel.getSatelliteSpaceState(gpslist[:, 0].reshape(-1))
+        # 计算轨道差距
+        statepoints_dist = statepoints - gpslist[:, 1:]
+        statepoints_dist_line = statepoints_dist[:, :3]
+        statepoints_dist_line = np.sum(statepoints_dist_line ** 2, axis=1) ** 0.5
+
+        statepoints_dist_ver = np.sum(statepoints_dist[:, 3:] ** 2, axis=1) ** 0.5
+        print(
+            "sata Point distance:\t{}\t -\t{}\t ".format(np.min(statepoints_dist_line), np.max(statepoints_dist_line)))
+
+        # 求解模型前计算常数变换模型
+        self.PrepareConvertSystem()
+        self.outParameterText(os.path.join(workspace_dir, "orth_para.txt"))
+
     def preCaldem_sar_rc(self,dem_path,ori_sar_path,work_path,taget_path):
         #.\SIMOrthoProgram\x64\Release\SIMOrthoProgram.exe 1 parameter_path     dem_path      ori_sar_path     work_path     taget_path  
         #                              SIMOrthoProgram.exe 1 in_parameter_path  in_dem_path  in_ori_sar_path  in_work_path  in_taget_path
@@ -3026,6 +3262,14 @@ class IndirectOrthorectification(Orthorectification):
     '''
 
 if __name__ == '__main__':
-    file_path = r"D:\BaiduNetdiskDownload\S1\20240726\ortho.tif"
-    out_path = r"D:\BaiduNetdiskDownload\S1\20240726\ortho_register.tif"
-    ScatteringAlg().lin_to_db(file_path, out_path)
+    # file_path = r"D:\BaiduNetdiskDownload\S1\20240726\ortho.tif"
+    # out_path = r"D:\BaiduNetdiskDownload\S1\20240726\ortho_register.tif"
+    # ScatteringAlg().lin_to_db(file_path, out_path)
+    # 正无穷大
+    positive_infinity = 1.0 / 0.0
+    print(positive_infinity)  # 输出: inf
+
+    # 负无穷大
+    negative_infinity = -1.0 / 0.0
+    print(negative_infinity)  # 输出: -inf
+

Ortho/OrthoMain.py

@@ -38,7 +38,7 @@ else:
     DEBUG = False
 EXE_NAME = cf.get('exe_name')
 productLevel = cf.get('productLEVEL')
-tager = '-' + cf.get('tager')
+tager = '-' + cf.get('target')
 #env_str = os.getcwd()
 env_str =os.path.dirname(os.path.abspath(sys.argv[0])) #os.path.split(os.path.realpath(__file__))[0]
 os.environ['PROJ_LIB'] = env_str
@@ -151,10 +151,12 @@ class OrthoMain:
         self.__input_paras = self.__alg_xml_handler.get_input_paras()
         # self.__output_paras = self.__alg_xml_handler.get_output_paras()
         self.__create_work_space()
+        self.__out_name = os.path.splitext(os.path.splitext(os.path.basename(self.__input_paras['SLC']['ParaValue']))[0])[0]
+
         self.__in_processing_paras = self.__init_processing_paras(self.__input_paras)       # 输入{paraname：paravalue}
         # self.__out_processing_paras = self.__init_processing_paras(self.__output_paras)       # 输入{paraname：paravalue}
 
-        self.__out_name = os.path.splitext(os.path.splitext(os.path.basename(self.__input_paras['SLC']['ParaValue']))[0])[0]
+
         # AlgorithmName = self.__alg_xml_handler.get_algorithm_name()
         # TaskId = self.__alg_xml_handler.get_task_id()
         result_name = self.__out_name + ".tar.gz"
@@ -290,8 +292,11 @@ class OrthoMain:
                     processing_paras.update({name: para_path})
                 if para['DataType'] == 'tar.gz':
                     para_path = os.path.join(self.__workspace_path, para['ParaValue'])
-                    tar_gz_dic = self.__dec_tar_gz(name, para_path, self.__workspace_unpack_path)
-                    processing_paras.update(tar_gz_dic)
+                    if 'TOPN' in self.__out_name:
+                        self.__TOP_dec_tar_gz(name, para_path, self.__workspace_unpack_path)
+                    else:
+                        tar_gz_dic = self.__dec_tar_gz(name, para_path, self.__workspace_unpack_path)
+                        processing_paras.update(tar_gz_dic)
                 if para['DataType'] == 'tif' or para['DataType'] == 'tiff':     # 新增修改dem数据为文件绝对路径
                     if para['ParaValue'] != 'empty' and para['ParaValue'] != 'Empty' and para['ParaValue'] != '':
                         para_path_list = para['ParaValue'].split(";")
@@ -323,6 +328,69 @@ class OrthoMain:
                     processing_paras.update({name: value})
         return processing_paras
 
+    def __TOP_dec_tar_gz(self, name1, tar_gz_path, out_dir):
+        """
+                解压.tar_gz格式景影像文件
+                :param tar_gz_path:.tar_gz文件路径
+                :param out_dir:输出文件夹
+                :return para_dic:全极化影像路径
+                """
+        # 创建文件夹
+        name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz')
+        file_dir = os.path.join(out_dir, name + '\\')
+        if os.path.exists(file_dir) is False:
+            os.makedirs(file_dir)
+        # 解压
+        t = tarfile.open(tar_gz_path)
+        t.extractall(path=file_dir)
+
+        para_dic = {}
+
+        tif_files = list(glob.glob(os.path.join(file_dir, '*.tiff')))
+        tif_files += list(glob.glob(os.path.join(file_dir, '*.tif')))
+        hh_list, hv_list, vh_list, vv_list, dh_list = [], [], [], [], []
+        for in_tif_path in tif_files:
+            file_baseName = os.path.splitext(os.path.basename(in_tif_path))[0]
+
+            if 'hh' in file_baseName or 'HH' in file_baseName:
+                hh_list.append(in_tif_path)
+                self.polsar_list.append('HH')
+            elif 'hv' in file_baseName or 'HV' in file_baseName:
+                hv_list.append(in_tif_path)
+                self.polsar_list.append('HV')
+            elif 'vh' in file_baseName or 'VH' in file_baseName:
+                vh_list.append(in_tif_path)
+                self.polsar_list.append('VH')
+            elif 'vv' in file_baseName or 'VV' in file_baseName:
+                vv_list.append(in_tif_path)
+                self.polsar_list.append('VV')
+            elif "DH" in os.path.basename(in_tif_path):
+                dh_list.append(in_tif_path)
+                self.polsar_list.append('DH')
+        self.polsar_list = list(set(self.polsar_list))
+        para_dic.update({'HH': hh_list})
+        para_dic.update({'HV': hv_list})
+        para_dic.update({'VH': vh_list})
+        para_dic.update({'VV': vv_list})
+        para_dic.update({'DH': dh_list})
+
+        # 获取文件夹内的文件
+
+        if os.path.exists(file_dir + name + '\\'):
+            meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.meta.xml')))
+            para_dic.update({'SLC': file_dir + name})
+        else:
+            meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.meta.xml')))
+            para_dic.update({'SLC': file_dir})
+
+        if meta_xml_paths == []:
+            raise Exception('there is not .meta.xml in path: ', file_dir + '\\')
+        para_dic.update({'META': meta_xml_paths[0]})
+        self.image_meta_xml = meta_xml_paths
+        # para_dic.update({name1: file_dir})    # {SLC: file_path}
+
+        return para_dic
+
     def __dec_tar_gz(self, name1, tar_gz_path, out_dir):
         """
         解压.tar_gz格式景影像文件
@@ -351,6 +419,7 @@ class OrthoMain:
         if meta_xml_paths == []:
             raise Exception('there is not .meta.xml in path: ', file_dir + '\\')
         para_dic.update({'META': meta_xml_paths[0]})
+        para_dic.update({'incXML': alg.get_incidence_xml_paths(file_dir, name)[0]})
         self.image_meta_xml = meta_xml_paths
         # para_dic.update({name1: file_dir})    # {SLC: file_path}
 
@@ -390,8 +459,10 @@ class OrthoMain:
 
         elif CorrectMethod.get('CorrectMethod') == '2' or CorrectMethod.get('CorrectMethod') == 2:
             logger.info("CorrectMethod is RD!")
-           
-            return self.RD_process_handle()
+            if 'TOPN' in self.__out_name:
+                return self.RD_TOP_process_handle()
+            else:
+                return self.RD_process_handle()
         else:
              raise Exception('No CorrectMethod')
         
@@ -595,11 +666,22 @@ class OrthoMain:
         ortho_bsMap_path = self.__workspace_baseMap_path
         bsMap_merged_path = DEMProcess.bsMap_merged(bsMap, meta_file_path, ortho_bsMap_path)
 
+
         dem_path = self.cut_dem(dem_merged_path, meta_file_path)
         # 2、间接定位法求解行列坐标
         slc_paths = self.__in_processing_paras["SLC"]
+
+        in_slc_path = None
+        for slc_path in os.listdir(slc_paths):
+            if slc_path.find(".tiff") > 0 and (
+                    slc_path.find("_HH_") > 0 or slc_path.find("_VV_") > 0 or slc_path.find("_DH_") > 0):
+                in_slc_path = os.path.join(slc_paths, slc_path)
+                break
+        inc_xml_path = self.__in_processing_paras['incXML']
+        rows = self.imageHandler.get_img_height(in_slc_path)
+        inc_path = os.path.join(self.__workspace_Temporary_path, 'inc_angle.tif')
+        ImageHandler.get_inc_angle(inc_xml_path, rows, 1, inc_path)
         # 2.1 生成映射表
-        slc_path=os.path.join(slc_paths,os.listdir(slc_paths)[0])
         
         # 2.2 生成局地入射角
         path2 = env_str
@@ -608,11 +690,7 @@ class OrthoMain:
         # 2.3 输出结果
 
         # 3 处理RD
-        in_slc_path=None
-        for slc_path in os.listdir(slc_paths):
-            if slc_path.find(".tiff")>0 and (slc_path.find("_HH_")>0 or slc_path.find("_VV_")>0 or slc_path.find("_DH_")>0):
-                in_slc_path=os.path.join(slc_paths,slc_path)
-                break
+
         # 获取校正模型后
         out_dir_path = self.__workspace_package_path.replace("\\", "\\\\")
         Orthorectification.preCaldem_sar_rc(dem_path,in_slc_path,self.__workspace_Temporary_path,self.__workspace_package_path.replace("\\","\\\\")) # 初步筛选坐标范围
@@ -675,10 +753,6 @@ class OrthoMain:
         GTC_rc_path=os.path.join(self.__workspace_package_path,"ori_sim-ortho.tif")
         GTC_out_path=self.__workspace_package_path
 
-
-        # dem_rc = os.path.join(self.__workspace_package_path, "sim_ori-ortho.tif")
-        # dem_rc_pro = self.process_sim_ori(this_in_rpc_lon_lat_path, dem_rc)
-        # shutil.move(dem_rc_pro, dem_rc)
         parameter_path = os.path.join(self.__workspace_package_path, "orth_para.txt")
         in_tif_paths = list(glob.glob(os.path.join(slc_paths, '*.tiff')))
         for in_tif_path in in_tif_paths:
@@ -686,7 +760,7 @@ class OrthoMain:
             slc_path_temp=os.path.join(slc_paths,in_tif_path)
             out_power_path=os.path.join(self.__workspace_Temporary_path,slc_path_temp.replace(".tiff","-lin.tif").replace("L1A","L1B")).replace("HH","h_h").replace("HV","h_v").replace("VH","v_h").replace("VV","v_v").replace("DH","d_h")
             # out_power_path=os.path.join(self.__workspace_Temporary_path,slc_path_temp.replace(".tiff","_db.tif"))
-            alg.sar_backscattering_coef(slc_path_temp,self.__in_processing_paras['META'],out_power_path)
+            alg.sar_backscattering_coef(slc_path_temp,self.__in_processing_paras['META'],out_power_path, inc_path)
 
             lin_tif_path = os.path.join(self.__workspace_Temporary_path,
                                         os.path.basename(out_power_path).split('-')[0] + "-lin_geo.tif")
@@ -761,6 +835,157 @@ class OrthoMain:
         return True        
         pass
 
+    def RD_TOP_process_handle(self):
+        # RPC
+        logger.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
+        # print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
+        # 1、DEM拼接、裁剪、重采样
+        Orth_Slc = []
+        in_dem_path = self.__in_processing_paras['DEM']
+        meta_file_path = self.__in_processing_paras['META']  # .meta文件路径
+        out_dem_path = self.__workspace_ResampledDEM_path
+        dem_merged_path = DEMProcess.dem_merged(in_dem_path, meta_file_path,
+                                                out_dem_path)  # 生成TestDEM\mergedDEM_VRT.tif
+
+        # bsMap = self.__in_processing_paras['baseMap']
+        # ortho_bsMap_path = self.__workspace_baseMap_path
+        # bsMap_merged_path = DEMProcess.bsMap_merged(bsMap, meta_file_path, ortho_bsMap_path)
+
+        dem_path = self.cut_dem(dem_merged_path, meta_file_path)
+        # 2、间接定位法求解行列坐标
+        sim_ori_map = {}
+        patamter_map = {}
+        inc_angle_dir = os.path.join(self.__workspace_Temporary_path, f"incAngle")
+        localincidentAngle_dir = os.path.join(self.__workspace_Temporary_path, f"localincidentAngle")
+        if not os.path.exists(inc_angle_dir):
+            os.makedirs(inc_angle_dir)
+        if not os.path.exists(localincidentAngle_dir):
+            os.makedirs(localincidentAngle_dir)
+        slc_paths = self.__in_processing_paras[self.polsar_list[0]]
+        for slc_path in slc_paths:
+            baseName = os.path.splitext(os.path.basename(slc_path))[0]
+
+            temp_dir = os.path.join(self.__workspace_Temporary_path, baseName)
+            if not os.path.exists(temp_dir):
+                os.makedirs(temp_dir)
+
+            path2 = env_str
+            Orthorectification = IndirectOrthorectification(os.path.join(path2, "config.yaml"))
+
+            Orthorectification.IndirectOrthorectification_top(self.__in_processing_paras["META"], slc_path,
+                                                              temp_dir)  # 改动1
+
+            Orthorectification.preCaldem_sar_rc(dem_path, slc_path, self.__workspace_Temporary_path, temp_dir)
+
+            strip_id = baseName.split('_')[-1]
+            RD_sim_ori = os.path.join(temp_dir, 'RD_sim_ori.tif')
+            sim_ori_ortho = os.path.join(self.__workspace_package_path, f"sim_ori_strip_{strip_id}-ortho.tif")
+            shutil.move(RD_sim_ori, sim_ori_ortho)
+            sim_ori_map.update({'sim_ori_strip_' + strip_id: sim_ori_ortho})
+
+            paramter_path = os.path.join(temp_dir, 'orth_para.txt')
+            patamter_map.update({'paramter_strip_' + strip_id: paramter_path})
+
+            inc_angle_strip = os.path.join(temp_dir, 'incidentAngle.tiff')
+            inc_angle_merged = os.path.join(inc_angle_dir, f"incidentAngle_Strip_{strip_id}.tif")
+            shutil.move(inc_angle_strip, inc_angle_merged)
+
+            localincidentAngle_strip = os.path.join(temp_dir, 'localincidentAngle.tiff')
+            localincidentAngle_merged = os.path.join(localincidentAngle_dir, f"localincidentAngle_Strip_{strip_id}.tif")
+            shutil.move(localincidentAngle_strip, localincidentAngle_merged)
+
+        DEMProcess.tif_merged(inc_angle_dir, self.__workspace_origin_path, self.__workspace_package_path)
+        DEMProcess.tif_merged(localincidentAngle_dir, self.__workspace_origin_path, self.__workspace_package_path)
+
+        tif_db_dir_list = []
+        for polr in self.polsar_list:
+            tifFiles = self.__in_processing_paras[polr]
+            tif_db_dir = os.path.join(self.__workspace_Temporary_path, f"{polr}_db")
+            if not os.path.exists(tif_db_dir):
+                os.makedirs(tif_db_dir)
+            tif_db_dir_list.append(tif_db_dir)
+            for tif in tifFiles:
+                baseName = os.path.splitext(os.path.basename(tif))[0]
+                strip_id = baseName.split('_')[-1]
+                inc_xml_path = os.path.join(os.path.dirname(slc_path), baseName + '.incidence.xml')
+                inc_path = os.path.join(self.__workspace_Temporary_path, baseName + '_angle.tif')
+                rows = self.imageHandler.get_img_height(slc_path)
+                ImageHandler.get_inc_angle(inc_xml_path, rows, 1, inc_path)
+
+                out_power_path = os.path.join(self.__workspace_Temporary_path,
+                                              os.path.basename(tif).replace(".tiff", "-lin.tif").replace("L1A",
+                                                                                                         "L1B")).replace(
+                    "HH", "h_h").replace("HV", "h_v").replace("VH", "v_h").replace("VV", "v_v").replace("DH", "d_h")
+
+                alg.sar_backscattering_coef(tif, self.__in_processing_paras['META'], out_power_path, inc_path)
+
+                lin_tif_path = os.path.join(self.__workspace_Temporary_path,
+                                            os.path.basename(out_power_path).split('-')[0] + "-lin_geo.tif")
+
+                Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(patamter_map[f"paramter_strip_{strip_id}"],
+                                                                           sim_ori_map[f"sim_ori_strip_{strip_id}"],
+                                                                           out_power_path,
+                                                                           lin_tif_path)
+                tempout_tif_path = os.path.join(tif_db_dir,
+                                                os.path.basename(lin_tif_path).split('-')[0] + '.tif')
+                alg.lin_to_db_top(lin_tif_path, tempout_tif_path)  # 线性值转回DB值
+
+        for tif_merged in tif_db_dir_list:
+            DEMProcess.tif_merged(tif_merged, self.__workspace_origin_path, self.__workspace_package_path)
+
+        for tiff_name in os.listdir(self.__workspace_package_path):
+            if (tiff_name.find(".tiff") > 0 or tiff_name.find(".tif") > 0) and 'sim_ori' not in tiff_name:
+                self.imageHandler.write_quick_view(os.path.join(self.__workspace_package_path, tiff_name))
+
+
+        tem_folder = self.__workspace_path + EXE_NAME + r"\Temporary""\\"
+        image_path = tempout_tif_path  # os.path.join(self.__workspace_package_path, "OrthoMapTable.tif")
+        out_path1 = os.path.join(tem_folder, "trans_geo_projcs.tif")
+        out_path2 = os.path.join(tem_folder, "trans_projcs_geo.tif")
+        model_path = "./product.xml"
+        meta_xml_path = os.path.join(self.__workspace_package_path,
+                                     os.path.basename(self.__out_para).replace(".tar.gz", ".meta.xml"))
+
+        para_dict = CreateMetaDict(image_path, self.__in_processing_paras['META'], self.__workspace_package_path,
+                                   out_path1, out_path2).calu_nature()
+
+        Azimuth = os.path.join(self.__workspace_Temporary_path, 'Azimuth.txt')
+        if os.path.exists(Azimuth):
+            Azimuth_incidence = OrthoAzimuth.get_Azimuth_incidence(Azimuth)
+        else:
+            logger.error("read Azimuth txt failed!")
+            Azimuth_incidence = 'None'
+
+        para_dict.update({"ObservationGeometry_SatelliteAzimuth": Azimuth_incidence})
+        para_dict.update({"imageinfo_ProductName": '正射校正'})
+        para_dict.update({"imageinfo_ProductIdentifier": 'Ortho'})
+        para_dict.update({"imageinfo_ProductLevel": productLevel})
+        para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
+        para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "DEM"})
+        CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
+
+        temp_folder = os.path.join(self.__workspace_path, EXE_NAME, 'Output')
+        out_xml = os.path.join(temp_folder, os.path.basename(meta_xml_path))
+        if os.path.exists(temp_folder) is False:
+            os.mkdir(temp_folder)
+        # CreateProductXml(para_dict, model_path, out_xml).create_standard_xml()
+        shutil.copy(meta_xml_path, out_xml)
+
+        # 生成压缩包
+        logger.info('progress bar :94％')
+        logger.info('start make targz..')
+        # self.del_floder(self.__workspace_unpack_path)
+        # self.del_floder(self.__workspace_ResampledDEM_path)
+        # self.del_floder(self.__workspace_LutImg_path)
+        # self.del_floder(self.__workspace_IncidenceImg_path)
+        # self.del_floder(self.__workspace_SimImg_path)
+        # self.del_floder(self.__workspace_SARIntensity_path)
+        self.make_targz(self.__out_para, self.__workspace_package_path + "\\")
+        logger.info('make targz finish')
+        logger.info('progress bar :100％')
+        return True
+        pass
+    
 
 if __name__ == '__main__':
     start = datetime.datetime.now()

Ortho/Ortho_C_SAR_V3.xml

@@ -49,7 +49,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaValue>
-         F:\isce_yuan\lancover-yuan\GF3B_KSC_QPSI_010328_E86.1_N44.5_20231109_L1A_AHV_L10000262134.tar.gz</ParaValue>
+         G:\UFS\GF3B_MYC_UFS_003192_E116.6_N39.5_20220702_L1A_DH_L10000059009.tar.gz</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>File</Control>
@@ -67,7 +67,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>F:\xibei_LandCover\dem.tif</ParaValue>
+        <ParaValue>G:\UFS\dem\115E39N_COP30.tif</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>True</EnMultipleChoice>
         <Control>File</Control>
@@ -86,7 +86,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaValue>
-         F:\isce_yuan\lancover-yuan\S1A_map</ParaValue>
+         G:\UFS\SIA_GBM</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>File</Control>
@@ -121,7 +121,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\ortho\Output\GF3B_KSC_QPSI_010328_E86.1_N44.5_20231109_L1A_AHV_L10000262134-ortho.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\ortho\Output\GF3B_MYC_UFS_003192_E116.6_N39.5_20220702_L1A_DH_L10000059009-ortho.tar.gz</ParaValue>
         <OptionValue>DEFAULT</OptionValue>
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>

Ortho/config.ini

@@ -2,7 +2,7 @@
 #  定义config分组
 [config]
 ######1-算法基本参数######
-tager = ortho
+target = ortho
 productLEVEL = 3
 # 算法名称。修改临时工作区生成临时文件的名称，日志名称；
 exe_name = ortho

backScattering/BackScatteringAlg.py

@@ -8,7 +8,9 @@
 @Date ：2021/8/14
 @Version ：1.0.0
 """
+import glob
 import zipfile
+from xml.etree.ElementTree import ElementTree
 
 # from re import I, T, X, match
 # from types import DynamicClassAttribute
@@ -57,10 +59,11 @@ class ScatteringAlg:
         pass
 
     @staticmethod
-    def sar_backscattering_coef(in_sar_tif, meta_file_path, out_sar_tif, replece_VV = False, is_DB = True):
+    def sar_backscattering_coef(in_sar_tif, meta_file_path, out_sar_tif, inc_xml_path, replece_VV = False, is_DB = True):
 
         # 读取原始SAR影像
         proj, geotrans, in_data = ImageHandler.read_img(in_sar_tif)
+        rows = ImageHandler.get_img_height(in_sar_tif)
 
         # 计算强度信息
         I = np.array(in_data[0], dtype="float32")
@@ -103,8 +106,12 @@ class ScatteringAlg:
         coef_arr[np.isinf(coef_arr)] = -9999
         coef_arr[where_9999_0] = -9999
         coef_arr[where_9999_1] = -9999
+        coef_arr[coef_arr < -25] = -25
         ## 输出的SAR后向散射系数产品
         # ImageHandler.write_img(out_sar_tif, proj, geotrans, coef_arr, 0)
+        _, _, inc_arr = ImageHandler().read_img(inc_xml_path)
+        rad_arr = np.radians(inc_arr)
+        sin_arr = np.sin(rad_arr)
 
         tif_array = np.power(10.0, coef_arr / 10.0)  # dB --> 线性值  后向散射系数
 
@@ -112,11 +119,27 @@ class ScatteringAlg:
         tif_array[np.isinf(tif_array)] = 0
         tif_array[where_9999_0] = 0
         tif_array[where_9999_1] = 0
-
+        tif_array_cos = tif_array / sin_arr
 
         ImageHandler.write_img(out_sar_tif, proj, geotrans, tif_array, 0)
+        # out_sar_tif1 = r'D:\micro\WorkSpace\BackScattering\cal_cos.tif'
+        # ImageHandler.write_img(out_sar_tif1, proj, geotrans, tif_array_cos, 0)
         return True
 
+    @staticmethod
+    def get_inc_angle(inc_xml, rows, out_path):
+        tree = ElementTree()
+        tree.parse(inc_xml)  # 影像头文件
+        root = tree.getroot()
+        values = root.findall('incidenceValue')
+        angle_value = [value.text for value in values]
+        angle_value = np.array(angle_value)
+        inc_angle = np.tile(angle_value, (rows, 1))
+        ImageHandler.write_img(out_path, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], inc_angle)
+
+        pass
+
+
     @staticmethod
     def sar_backscattering_coef_RPC(in_sar_tif, meta_file_path, out_sar_tif, replece_VV=False, is_DB=True):
 
@@ -177,6 +200,17 @@ class ScatteringAlg:
         # db_arr[np.isinf(db_arr)] = -9999
         ImageHandler.write_img(db_path, proj, geotrans, db_arr, -9999)
 
+    @staticmethod
+    def get_incidence_xml_paths(file_dir, name):
+        meta_xml_paths = []
+        if os.path.exists(file_dir + name + '\\'):
+            meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.incidence.xml')))
+        else:
+            meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.incidence.xml')))
+        if meta_xml_paths is None or meta_xml_paths == []:
+            raise Exception('there is not .incidence.xml in path: ', file_dir + '\\')
+        return meta_xml_paths
+
 
 def FindInfomationFromJson(HeaderFile_dom_json, node_path_list):
     """

backScattering/BackScatteringMain.py

@@ -185,7 +185,7 @@ class ScatteringMain:
         else:
             meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.meta.xml')))
             para_dic.update({'SLC': file_dir})
-
+        para_dic.update({'incXML': alg.get_incidence_xml_paths(file_dir, name)[0]})
         if meta_xml_paths == []:
             raise Exception('there is not .meta.xml in path: ', file_dir + '\\')
         para_dic.update({'META': meta_xml_paths[0]})
@@ -376,6 +376,10 @@ class ScatteringMain:
         Orth_Slc = []
         in_dem_path = self.__in_processing_paras['DEM']
         meta_file_path = self.__in_processing_paras['META']  # .meta文件路径
+        inc_xml_path = self.__in_processing_paras['incXML']
+        rows = self.imageHandler.get_img_height(in_tif_paths[0])
+        inc_path = os.path.join(self.__workspace_preprocessing_path, 'inc_angle.tif')
+        ImageHandler.get_inc_angle(inc_xml_path, rows,1, inc_path)
         out_dem_path = self.__workspace_preprocessing_path
         # unzipped_folder_path = DEMProcess.unzip_file(in_dem_path, self.__workspace_origin_path)  # 解压DEM.zip
         dem_merged_path = DEMProcess.dem_merged(in_dem_path, meta_file_path,
@@ -470,7 +474,7 @@ class ScatteringMain:
             if ('HH' in os.path.basename(in_tif_path)) or ('HV' in os.path.basename(in_tif_path)) or (
                     'VH' in os.path.basename(in_tif_path)) or ('VV' in os.path.basename(in_tif_path)) or (
                     'DH' in os.path.basename(in_tif_path)):
-                alg.sar_backscattering_coef(in_tif_path, meta_file_path, out_tif_path)
+                alg.sar_backscattering_coef(in_tif_path, meta_file_path, out_tif_path, inc_path)
                 # 构建RPC
                 # 查找RPC
                 rpc_path = in_tif_path.replace(".tiff", ".rpc") if os.path.exists(
@@ -494,6 +498,7 @@ class ScatteringMain:
                 Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, sim_ori_rpc,
                                                                            out_tif_path,
                                                                            lin_tif_path)
+
                 tempout_tif_path = os.path.join(self.__workspace_processing_path,
                                                 os.path.splitext(os.path.basename(in_tif_path))[0]) + tager + r".tif"
                 alg.lin_to_db(lin_tif_path, tempout_tif_path)  # 线性值转回DB值

backScattering/BackScattering_C_SAR_V3.xml

@@ -42,7 +42,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>F:\MicroWorkspace\20240826Ortho\GF3B_MH1_UFS_012705_E116.4_N44.2_20240422_L1A_DH_L10000343416.tar.gz</ParaValue>
+        <ParaValue>F:\MicroWorkspace\Micro\neimenggu\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L1A_AHV_L10006708819.tar.gz</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -55,9 +55,9 @@
         <ParaChsName>DEM数字高程影像</ParaChsName>
         <Description>30m分辨率DEM数字高程影像</Description>
         <ParaType>File</ParaType>
-        <DataType>File</DataType>
+        <DataType>tif</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>F:\MicroWorkspace\20240826Ortho\dem</ParaValue>
+        <ParaValue>F:\MicroWorkspace\Micro\neimenggu\DEM\115E39N_COP30.tif</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>True</EnMultipleChoice> 
         <Control>File</Control> 
@@ -72,7 +72,7 @@
         <ParaType>File</ParaType>
         <DataType>File</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>F:\MicroWorkspace\20240826Ortho\S1GBM</ParaValue>
+        <ParaValue>F:\MicroWorkspace\Micro\neimenggu\S1GBM</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>True</EnMultipleChoice>
         <Control>File</Control>
@@ -89,7 +89,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\BackScattering\Output\GF3B_MH1_UFS_012705_E116.4_N44.2_20240422_L1A_DH_L10000343416-cal.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\BackScattering\Output\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L1A_AHV_L10006708819-cal.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

dem-C-SAR/Dem.xml

@@ -38,7 +38,7 @@
                 <ParaType>Value</ParaType>
                 <DataType>string</DataType>
                 <ParaSource>Man</ParaSource>
-                <ParaValue>20161129</ParaValue>
+                <ParaValue>20200218</ParaValue>
                 <EnModification>True</EnModification>
                 <EnMultipleChoice>False</EnMultipleChoice>
                 <Control>UploadInput</Control>
@@ -54,8 +54,8 @@
                 <DataType>zip</DataType>
                 <ParaSource>Man</ParaSource>
                 <ParaValue>
-                    D:\micro\microproduct_depdence\GF3-Deformation\download\cls\GF3_SAY_FSI_001614_E113.2_N34.5_20161129_L1A_HHHV_L10002015686.tar.gz;
-          D:\micro\microproduct_depdence\GF3-Deformation\download\cls\GF3_KAS_FSI_002034_E113.4_N34.7_20161228_L1A_HHHV_L10002077539.tar.gz
+                    G:\GF3极化干涉数据\2\GF3_SAY_QPSI_018565_E104.1_N30.6_20200218_L1A_AHV_L10004624982.tar.gz;
+                    G:\GF3极化干涉数据\2\GF3_SAY_QPSI_018983_E104.1_N30.6_20200318_L1A_AHV_L10004680879.tar.gz
                 </ParaValue>
                 <EnModification>True</EnModification>
                 <EnMultipleChoice>False</EnMultipleChoice>
@@ -71,7 +71,7 @@
                 <ParaType>value</ParaType>
                 <DataType>string</DataType>
                 <ParaSource>Man</ParaSource>
-                <ParaValue>34.6;34.63;113.1;113.15</ParaValue>
+                <ParaValue>empty</ParaValue>
                 <EnModification>True</EnModification>
                 <EnMultipleChoice>True</EnMultipleChoice>
                 <Control>UploadInput</Control>
@@ -86,7 +86,7 @@
                 <ParaType>File</ParaType>
                 <DataType>File</DataType>
                 <ParaSource>Man</ParaSource>
-                <ParaValue>D:\micro\microproduct_depdence\GF3-Deformation\dem</ParaValue>
+                <ParaValue>G:\GF3极化干涉数据\DEM</ParaValue>
                 <EnModification>True</EnModification>
                 <EnMultipleChoice>False</EnMultipleChoice>
                 <Control>File</Control>
@@ -120,7 +120,7 @@
                 <ParaType>File</ParaType>
                 <DataType>tar.gz</DataType>
                 <ParaSource>Cal</ParaSource>
-                <ParaValue>D:\micro\WorkSpace\Dem\Output\GF3_SAY_FSI_001614_E113.2_N34.5_20161129_L1A_HHHV_L10002015686-DEM.tar.gz</ParaValue>
+                <ParaValue>D:\micro\WorkSpace\Dem\Output\GF3_SAY_QPSI_018565_E104.1_N30.6_20200218_L1A_AHV_L10004624982-DEM.tar.gz</ParaValue>
             </Parameter>
         </Outputs>
     </AlgCompt>

dem-C-SAR/DemMain.py

@@ -562,7 +562,7 @@ if __name__ == '__main__':
     start = datetime.datetime.now()
     try:
         if len(sys.argv) < 2:
-            xml_path = r'Dem_C_SAR_V3.xml'
+            xml_path = r'Dem.xml'
         else:
             xml_path = sys.argv[1]
         Main = DemMain(xml_path)
@@ -583,3 +583,4 @@ if __name__ == '__main__':
 
 
 
+

dem-C-SAR/DemMain.spec

@@ -1,6 +1,30 @@
 # -*- mode: python ; coding: utf-8 -*-
 
+import sys
+from shutil import copy
+import os
 
+
+cwdpath = os.getcwd()
+toolDir = os.path.join(cwdpath, 'tool')
+if os.path.exists(toolDir):
+    os.remove(toolDir)
+os.mkdir(toolDir)
+source_folder = '../tool'
+
+def copy_file(path_read, path_write):
+    names = os.listdir(path_read)
+    for name in names:
+        path_read_new = os.path.join(path_read, name)
+        path_write_new = os.path.join(path_write, name)
+        if os.path.isdir(path_read_new):
+            if not os.path.exists(path_write_new):
+                os.mkdir(path_write_new)
+            copy_file(path_read_new, path_write_new)
+        else:
+            copy(path_read_new, path_write_new)
+
+copy_file(source_folder, toolDir)
 block_cipher = None
 
 

dem-C-SAR/ISCEApp/_internal/isce/components/isceobj/Location/Offset.py

@@ -395,117 +395,119 @@ class OffsetField(Component):
 
         numCoeff = 0
         ####Try and use Howard's polynomial fit code whenever possible
-        if (rangeOrder == azimuthOrder) and (rangeOrder <= 3):
-            if (rangeOrder == 1):
-                if maxOrder:
-                    numCoeff = 3
-                else:
-                    numCoeff = 4
-            elif (rangeOrder == 2):
-                if maxOrder:
-                    numCoeff = 6
-            elif (rangeOrder == 3):
-                if maxOrder:
-                    numCoeff = 10
+        try:
+            if (rangeOrder == azimuthOrder) and (rangeOrder <= 3):
+                if (rangeOrder == 1):
+                    if maxOrder:
+                        numCoeff = 3
+                    else:
+                        numCoeff = 4
+                elif (rangeOrder == 2):
+                    if maxOrder:
+                        numCoeff = 6
+                elif (rangeOrder == 3):
+                    if maxOrder:
+                        numCoeff = 10
+
+            inArr = np.array(self.unpackOffsets())
+            azmin = np.min(inArr[:, 2])
+            inArr[:, 2] -= azmin
+
+            ####Use Howard's code
+            if (numCoeff != 0) and not usenumpy:
+                x = list(inArr[:, 0])
+                y = list(inArr[:, 2])
+                dx = list(inArr[:, 1])
+                dy = list(inArr[:, 3])
+                sig = list(inArr[:, 4])
+
+                ####Range Offsets
+                obj = Offsetpoly()
+                obj.setLocationAcross(x)
+                obj.setLocationDown(y)
+                obj.setSNR(sig)
+                obj.setOffset(dx)
+                obj.numberFitCoefficients = numCoeff
+                obj.offsetpoly()
+
+                val = obj.offsetPoly
+
+                #####Unpack into 2D array
+                if numCoeff == 3:
+                    coeffs = [[val[0], val[1]],
+                              [val[2], 0.0]]
+
+                elif numCoeff == 4:
+                    coeffs = [[val[0], val[1]],
+                              [val[2], val[3]]]
+
+                elif numCoeff == 6:
+                    coeffs = [[val[0], val[1], val[4]],
+                              [val[2], val[3], 0.0],
+                              [val[5], 0.0, 0.0]]
+
+                elif numCoeff == 10:
+                    ####Unpacking needs to be checked.
+                    coeffs = [[val[0], val[1], val[4], val[8]],
+                              [val[2], val[3], val[6], 0.0],
+                              [val[5], val[7], 0.0, 0.0],
+                              [val[9], 0.0, 0.0, 0.0]]
+
+                rangePoly = Poly2D.Poly2D()
+                rangePoly.setMeanAzimuth(azmin)
+                rangePoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder, coeffs=coeffs)
+
+                ####Azimuth Offsets
+                obj.setOffset(dy)
+                obj.offsetpoly()
+                val = obj.offsetPoly
+
+                #####Unpack into 2D array
+                if numCoeff == 3:
+                    coeffs = [[val[0], val[1]],
+                              [val[2], 0.0]]
+
+                elif numCoeff == 4:
+                    coeffs = [[val[0], val[1]],
+                              [val[2], val[3]]]
+
+                elif numCoeff == 6:
+                    coeffs = [[val[0], val[1], val[4]],
+                              [val[2], val[3], 0.0],
+                              [val[5], 0.0, 0.0]]
+
+                elif numCoeff == 10:
+                    ####Unpacking needs to be checked.
+                    coeffs = [[val[0], val[1], val[4], val[8]],
+                              [val[2], val[3], val[6], 0.0],
+                              [val[5], val[7], 0.0, 0.0],
+                              [val[9], 0.0, 0.0, 0.0]]
+
+                azimuthPoly = Poly2D.Poly2D()
+                azimuthPoly.setMeanAzimuth(azmin)
+                azimuthPoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder, coeffs=coeffs)
+
+            ####Fallback to numpy based polynomial fitting
+            else:
+
+                x = inArr[:, 0]
+                y = inArr[:, 2]
+                dx = inArr[:, 1]
+                dy = inArr[:, 3]
+                sig = inArr[:, 4]
+
+                azimuthPoly = Poly2D.Poly2D()
+                azimuthPoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder)
+                azimuthPoly.polyfit(x, y, dy, sig=sig)
+                azimuthPoly._meanAzimuth += azmin
+
+                rangePoly = Poly2D.Poly2D()
+                rangePoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder)
+                rangePoly.polyfit(x, y, dx, sig=sig)
+                rangePoly._meanAzimuth += azmin
+            return (azimuthPoly, rangePoly)
+        except Exception as e:
+            print(e)
 
 
-        inArr = np.array(self.unpackOffsets())
-        azmin = np.min(inArr[:,2])
-        inArr[:,2] -= azmin
 
-        ####Use Howard's code
-        if (numCoeff != 0) and not usenumpy:
-            x = list(inArr[:,0])
-            y = list(inArr[:,2])
-            dx = list(inArr[:,1])
-            dy = list(inArr[:,3])
-            sig = list(inArr[:,4])
-
-            ####Range Offsets
-            obj = Offsetpoly()
-            obj.setLocationAcross(x)
-            obj.setLocationDown(y)
-            obj.setSNR(sig)
-            obj.setOffset(dx)
-            obj.numberFitCoefficients = numCoeff
-            obj.offsetpoly()
-
-            val = obj.offsetPoly
-
-            #####Unpack into 2D array
-            if numCoeff == 3:
-                coeffs = [[val[0], val[1]],
-                          [val[2], 0.0]]
-
-            elif numCoeff == 4:
-                coeffs = [[val[0], val[1]],
-                          [val[2], val[3]]]
-
-            elif numCoeff == 6:
-                coeffs = [[val[0], val[1], val[4]],
-                          [val[2], val[3], 0.0],
-                          [val[5], 0.0, 0.0]]
-
-            elif numCoeff == 10:
-                ####Unpacking needs to be checked.
-                coeffs = [[val[0], val[1], val[4], val[8]],
-                          [val[2], val[3], val[6], 0.0],
-                          [val[5], val[7],0.0, 0.0],
-                          [val[9], 0.0, 0.0, 0.0]]
-
-
-            rangePoly = Poly2D.Poly2D()
-            rangePoly.setMeanAzimuth(azmin)
-            rangePoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder, coeffs=coeffs)
-
-            ####Azimuth Offsets
-            obj.setOffset(dy)
-            obj.offsetpoly()
-            val = obj.offsetPoly
-
-            #####Unpack into 2D array
-            if numCoeff == 3:
-                coeffs = [[val[0], val[1]],
-                          [val[2], 0.0]]
-
-            elif numCoeff == 4:
-                coeffs = [[val[0], val[1]],
-                          [val[2], val[3]]]
-
-            elif numCoeff == 6:
-                coeffs = [[val[0], val[1], val[4]],
-                          [val[2], val[3], 0.0],
-                          [val[5], 0.0, 0.0]]
-
-            elif numCoeff == 10:
-                ####Unpacking needs to be checked.
-                coeffs = [[val[0], val[1], val[4], val[8]],
-                          [val[2], val[3], val[6], 0.0],
-                          [val[5], val[7],0.0, 0.0],
-                          [val[9], 0.0, 0.0, 0.0]]
-
-            azimuthPoly = Poly2D.Poly2D()
-            azimuthPoly.setMeanAzimuth(azmin)
-            azimuthPoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder, coeffs=coeffs)
-
-        ####Fallback to numpy based polynomial fitting
-        else:
-
-            x = inArr[:,0]
-            y = inArr[:,2]
-            dx = inArr[:,1]
-            dy = inArr[:,3]
-            sig = inArr[:,4]
-
-
-            azimuthPoly = Poly2D.Poly2D()
-            azimuthPoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder)
-            azimuthPoly.polyfit(x,y,dy, sig=sig)
-            azimuthPoly._meanAzimuth += azmin
-
-            rangePoly = Poly2D.Poly2D()
-            rangePoly.initPoly(rangeOrder=rangeOrder, azimuthOrder=azimuthOrder)
-            rangePoly.polyfit(x,y,dx,sig=sig)
-            rangePoly._meanAzimuth += azmin
-
-        return (azimuthPoly, rangePoly)

dem-C-SAR/geocoding.py

@@ -133,7 +133,7 @@ def geoCoding(tree,X_min,X_max,Y_min,Y_max,block_size,value_data,target_arr):
             X_ids=XY_query[idx,0] # x
             Y_ids=XY_query[idx,1] # y
             XY_query=np.concatenate([X_ids.reshape(-1,1),Y_ids.reshape(-1,1)],axis=1)
-            dist_mask=dist[:,3]<=50 ####################################################################################
+            dist_mask=dist[:,3]<=2 ####################################################################################
             idx=np.where(dist_mask==1)
             ind=ind[idx,:][0,:,:]
             dist=dist[idx,:][0,:,:]
@@ -220,12 +220,12 @@ def get_Dem(isce_work_path, temp_work_path, pack_path, product_name, lamda):
     los_data=read_tiff_dataset(los_tiff_path,band_idx=0)
     lon_data=read_tiff_dataset(lon_tiff_path,band_idx=0)
     lon = lon_data[0, :]
-    lon_min = np.min(lon)
-    lon_max = np.max(lon)
+    lon_min = np.min(lon_data)
+    lon_max = np.max(lon_data)
     lat_data=read_tiff_dataset(lat_tiff_path,band_idx=0)
     lat = lat_data[:, lat_data.shape[1]-1]
-    lat_min = np.min(lat)
-    lat_max = np.max(lat)
+    lat_min = np.min(lat_data)
+    lat_max = np.max(lat_data)
     out_detrend_path = os.path.join(temp_work_path, "detrend_unw.tif")
     unw_data = detrend_2d(unw_tiff_path, filt_topophase_cor_path, out_detrend_path)
     # unw_data=read_tiff_dataset(unw_tiff_path,band_idx=0)
@@ -250,7 +250,7 @@ def get_Dem(isce_work_path, temp_work_path, pack_path, product_name, lamda):
     
     # 形变值计算
     def_data=-1*unw_data*lamda/(4*math.pi)
-    hgt_data=hgt_data+def_data  # 叠加isce高程
+    hgt_data= hgt_data + def_data  # 叠加isce高程
     # hgt_data=def_data  # 叠加isce高程
     hgt_tiff_path = os.path.join(temp_work_path, "hgt_data.tiff")
     # ImageHandler.write_img(hgt_tiff_path, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], hgt_data)
@@ -264,7 +264,7 @@ def get_Dem(isce_work_path, temp_work_path, pack_path, product_name, lamda):
     inv_gt=gdal.InvGeoTransform(gt)
     print(xsize,ysize)
 
-    def_arr=np.zeros((int(ysize)+1,int(xsize)+1))-9999
+    # def_arr=np.zeros((int(ysize)+1,int(xsize)+1))-9999
     dem_arr=np.zeros((int(ysize)+1,int(xsize)+1))-9999
 
     X=inv_gt[0]+lon_data*inv_gt[1]+inv_gt[2]*lat_data
@@ -280,7 +280,7 @@ def get_Dem(isce_work_path, temp_work_path, pack_path, product_name, lamda):
     Y_min=math.ceil(np.min(Y))
     Y_max=math.floor(np.max(Y))
 
-    block_size=1000
+    block_size=3000
 
     dem_arr=geoCoding(tree,X_min,X_max,Y_min,Y_max,block_size,hgt_data,dem_arr)
     if not os.path.exists(pack_path):
@@ -292,9 +292,9 @@ def get_Dem(isce_work_path, temp_work_path, pack_path, product_name, lamda):
     return dem_target_data_path
 
 if __name__ == '__main__':
-    isce_work_path = r"D:\micro\microproduct_depdence\GF3-Deformation\isce_work"
-    temp_work_path = r"D:\micro\microproduct_depdence\GF3-Deformation\isce_work\test"
-    out_work_path =  r"D:\micro\microproduct_depdence\GF3-Deformation\isce_work\test"
-    product = r'D:\micro\microproduct_depdence\GF3-Deformation\isce_work\test\dem.tiff'
-    get_Dem(isce_work_path, temp_work_path, out_work_path, product)
+    isce_work_path = r"D:\micro\WorkSpace\Dem\Temporary\processing\isce_workspace"
+    temp_work_path = r"D:\micro\WorkSpace\Dem\Temporary\processing\isce_workspace\test"
+    out_work_path =  r"D:\micro\WorkSpace\Dem\Temporary\processing\isce_workspace\test"
+    product = r'D:\micro\WorkSpace\Dem\Temporary\processing\isce_workspace\test\dem.tiff'
+    get_Dem(isce_work_path, temp_work_path, out_work_path, product, 0.055517)
     pass

dem-sentiral/geocoding.py

@@ -115,7 +115,7 @@ def geoCoding(tree,X_min,X_max,Y_min,Y_max,block_size,value_data,target_arr):
             X_ids=XY_query[idx,0] # x
             Y_ids=XY_query[idx,1] # y
             XY_query=np.concatenate([X_ids.reshape(-1,1),Y_ids.reshape(-1,1)],axis=1)
-            dist_mask=dist[:,3]<=50 ####################################################################################
+            dist_mask=dist[:,3]<=1 ####################################################################################
             idx=np.where(dist_mask==1)
             ind=ind[idx,:][0,:,:]
             dist=dist[idx,:][0,:,:]

landcover_c_sar/LandCoverMain.py

@@ -135,6 +135,7 @@ class LandCoverMain:
         para_dic.update(InitPara.get_meta_dic_new(InitPara.get_meta_paths(file_dir, name), name))
         # tif路径字典
         para_dic.update(InitPara.get_polarization_mode(InitPara.get_tif_paths(file_dir, name)))
+        para_dic.update({'incXML': InitPara.get_incidence_xml_paths(file_dir, name)[0]})
         parameter_path = os.path.join(file_dir, "orth_para.txt")
         para_dic.update({"paraMeter": parameter_path})
         return para_dic
@@ -500,7 +501,13 @@ class LandCoverMain:
         polarization = ['HH', 'HV', 'VH', 'VV']
         calibration = Calibration.get_Calibration_coefficient(self.__processing_paras['Origin_META'], polarization)
         tif_path = atp.calibration(calibration, in_ahv_dir=self.__workspace_preprocessed_path)
-        atp.ahv_to_polsarpro_t3_soil(t3_path, tif_path)
+
+        inc_path = os.path.join(self.__workspace_processing_path, 'inc_angle.tif')
+        in_tif_paths = list(glob.glob(os.path.join(tif_path, '*.tif')))
+        rows = ImageHandler.get_img_height(in_tif_paths[0])
+        ImageHandler.get_inc_angle(self.__processing_paras['incXML'], rows, 1, inc_path)
+
+        atp.ahv_to_polsarpro_t3_soil(t3_path, inc_path, tif_path)
 
         # Lee滤波
         leeFilter = LeeRefinedFilterT3()

landcover_c_sar/LandCoverMain.spec

@@ -1,6 +1,30 @@
 # -*- mode: python ; coding: utf-8 -*-
 
+import sys
+from shutil import copy
+import os
 
+
+cwdpath = os.getcwd()
+toolDir = os.path.join(cwdpath, 'tool')
+if os.path.exists(toolDir):
+    os.remove(toolDir)
+os.mkdir(toolDir)
+source_folder = '../tool'
+
+def copy_file(path_read, path_write):
+    names = os.listdir(path_read)
+    for name in names:
+        path_read_new = os.path.join(path_read, name)
+        path_write_new = os.path.join(path_write, name)
+        if os.path.isdir(path_read_new):
+            if not os.path.exists(path_write_new):
+                os.mkdir(path_write_new)
+            copy_file(path_read_new, path_write_new)
+        else:
+            copy(path_read_new, path_write_new)
+
+copy_file(source_folder, toolDir)
 block_cipher = None
 
 

landcover_c_sar/LandCover_C_SAR_V3.xml

@@ -42,7 +42,7 @@
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
         <ParaValue>
-          F:\isce_yuan\lancover-yuan\GF3B_KSC_QPSI_010328_E86.1_N44.5_20231109_L1A_AHV_L10000262134-ortho.tar.gz</ParaValue>
+          F:\MicroWorkspace\GF3A_nanjing\input-ortho\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho.tar.gz</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>File</Control>
@@ -60,7 +60,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>F:\isce_yuan\lancover-yuan\train_landConver1.csv</ParaValue>
+        <ParaValue>F:\MicroWorkspace\GF3A_nanjing\input-ortho\LandCoverLable_geo_5.csv</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>True</EnMultipleChoice>
         <Control>UploadTable</Control>
@@ -99,7 +99,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Man</ParaSource>
-        <ParaValue>0,1,2</ParaValue>
+        <ParaValue>0,1,2,3,4,5,6</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>True</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -120,7 +120,7 @@
         <MaxValue>DEFAULT</MaxValue>
         <NoDataValue>DEFAULT</NoDataValue>
         <ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\LandCover\Output\GF3B_KSC_QPSI_010328_E86.1_N44.5_20231109_L1A_AHV_L10000262134-ortho-LANDCLASS.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\LandCover\Output\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho-LANDCLASS.tar.gz</ParaValue>
       </Parameter>
     </Outputs>
   </AlgCompt>

leafAreaIndex/LeafAreaIndex.xml

@@ -53,7 +53,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-cal.tar.gz</ParaValue>
+		<ParaValue>D:\micro\WorkSpace\BackScattering\Output\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L1A_AHV_L10006708819-cal.tar.gz</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -83,7 +83,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-cal-SMC.tif</ParaValue>
+        <ParaValue>F:\al_zhongji\C-SAR-data\CSAR_leaf_NEW_soil_moisture.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -96,9 +96,9 @@
         <ParaChsName>叶面积指数实测数据</ParaChsName>
         <Description>叶面积指数实测数据</Description>
         <ParaType>File</ParaType>
-        <DataType>tif</DataType>
+        <DataType>csv</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>F:\Tian-GF3-Wenchang\LAI\LAi.csv</ParaValue>
+        <ParaValue>F:\al_zhongji\C-SAR-data\CSAR_leaf_new_LAI.csv</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>File</Control>
@@ -113,7 +113,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Cal</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\NDVI\S2_NDVImed_SMC_GF3.tif</ParaValue>
+		<ParaValue>F:\MicroWorkspace\Micro\neimenggu\L9NDVI.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -128,7 +128,7 @@
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>0;1</ParaValue>
+		<ParaValue>-1;1</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -143,7 +143,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Cal</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\landCover.tif</ParaValue>
+		<ParaValue>F:\MicroWorkspace\Micro\neimenggu\N50_40_2020LC030_geo.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -158,7 +158,7 @@
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>empty</ParaValue>
+		<ParaValue>10;20;30;40</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -173,7 +173,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Cal</ParaSource>
-		<ParaValue>-31.6</ParaValue>
+		<ParaValue>0.0118770619</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -188,7 +188,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Cal</ParaSource>
-		<ParaValue>0.01</ParaValue>
+		<ParaValue>1.15098022</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -203,7 +203,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Cal</ParaSource>
-		<ParaValue>0.001</ParaValue>
+		<ParaValue>-0.146299064</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -218,7 +218,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Cal</ParaSource>
-		<ParaValue>-5</ParaValue>
+		<ParaValue>565.461513</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -235,7 +235,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Cal</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\LeafAreaIndex\Output\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-cal-LAI.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\LeafAreaIndex\Output\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L1A_AHV_L10006708819-cal-LAI.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

leafAreaIndex/LeafAreaIndex_C_SAR_V3.xml

@@ -60,7 +60,7 @@
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
         <ParaValue>
-          E:\MicroWorkspace\GF3A_nanjing\soilMoisture_data\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_back.tar.gz</ParaValue>
+          F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-cal.tar.gz</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>File</Control>
@@ -96,7 +96,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\TOP\SoilMoisture11\Temporary\processing\soil_moisture.tif</ParaValue>
+        <ParaValue>F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-cal-SMC.tif</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadTable</Control>
@@ -114,7 +114,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>D:\estar-proj\microproduct_depdence\model_tool\process_lai\laiplus2.csv</ParaValue>
+        <ParaValue>F:\Tian-GF3-Wenchang\LAI\LAi.csv</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadTable</Control>
@@ -132,7 +132,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\MicroWorkspace\GF3A_nanjing\soilMoisture_data\NDVI_E118.9_N31.5.tif</ParaValue>
+        <ParaValue>F:\Tian-GF3-Wenchang\NDVI\S2_NDVImed_SMC_GF3.tif</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadTable</Control>
@@ -150,7 +150,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Man</ParaSource>
-        <ParaValue>0;1</ParaValue>
+        <ParaValue>0.2;1</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -168,7 +168,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\MicroWorkspace\GF3A_nanjing\soilMoisture_data\Covering_preprocessed.tif</ParaValue>
+        <ParaValue>F:\Tian-GF3-Wenchang\LandCover.tif</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadTable</Control>
@@ -187,7 +187,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Man</ParaSource>
-        <ParaValue>10;20;30;40;50;60;80;90</ParaValue>
+        <ParaValue>10;20;30;40</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -205,7 +205,7 @@
         <OptionValue>DEFAULT</OptionValue>
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
-        <ParaValue>-31.6</ParaValue>
+        <ParaValue>0.0118770619</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -223,7 +223,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Man</ParaSource>
-        <ParaValue>0.01</ParaValue>
+        <ParaValue>1.15098022</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -241,7 +241,7 @@
         <OptionValue>DEFAULT</OptionValue>
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
-        <ParaValue>0.001</ParaValue>
+        <ParaValue>-0.146299064</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -259,7 +259,7 @@
         <MinValue>DEFAULT</MinValue>
         <MaxValue>DEFAULT</MaxValue>
         <ParaSource>Man</ParaSource>
-        <ParaValue>-5</ParaValue>
+        <ParaValue>565.461513</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>False</EnMultipleChoice>
         <Control>UploadInput</Control>
@@ -276,8 +276,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>
-          D:\micro\WorkSpace\LeafAreaIndex\Output\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_back-lef.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\LeafAreaIndex\Output\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-cal-LAI.tar.gz</ParaValue>
           <OptionValue>DEFAULT</OptionValue>
           <MinValue>DEFAULT</MinValue>
           <MaxValue>DEFAULT</MaxValue>

leafAreaIndex/LeafIndexMain.py

@@ -38,8 +38,8 @@ from scipy.interpolate import griddata
 import sys
 import multiprocessing
 from tool.file.fileHandle import fileHandle
-from sample_process import read_sample_csv,combine_sample_attr,ReprojectImages2,read_tiff,check_sample,split_sample_list
-from tool.LAI.LAIProcess import train_WMCmodel,test_WMCModel,process_tiff
+from sample_process import read_sample_csv,combine_sample_attr,ReprojectImages2,read_tiff,check_sample, train_WMCmodel
+# from tool.LAI.LAIProcess import train_WMCmodel,test_WMCModel,process_tiff
 
 cover_id_list = []
 threshold_of_ndvi_min = 0
@@ -227,12 +227,13 @@ class LeafIndexMain:
         # 利用角度为nan生成Mask
         pp.check_LocalIncidenceAngle(self.__preprocessed_paras['LocalIncidenceAngle'], self.__preprocessed_paras['LocalIncidenceAngle'])
         angle_nan_mask_path = processing_path + 'angle_nan_mask.tif'
-        roi.trans_tif2mask(angle_nan_mask_path, self.__preprocessed_paras['LocalIncidenceAngle'], np.nan)
+        roi.trans_tif2mask(angle_nan_mask_path, self.__preprocessed_paras['LocalIncidenceAngle'], 0, 90)
         # 利用影像的有效范围生成MASK
         tif_nan_mask_path = processing_path + 'tif_mask_nan.tif'
-        roi.trans_tif2mask(tif_nan_mask_path, self.__preprocessed_paras[self.__sar_tif_name], np.nan)
-        # tif_zero_mask_path = processing_path + 'tif_mask_zero.tif'
-        # roi.trans_tif2mask(tif_zero_mask_path, self.__preprocessed_paras[self.__sar_tif_name], 0,0)
+        # roi.trans_tif2mask(tif_nan_mask_path, self.__preprocessed_paras[self.__sar_tif_name], np.nan)
+        roi.trans_tif2mask(tif_nan_mask_path, self.__preprocessed_paras[self.__sar_tif_name], 0, 0)  # 0，0修改为np.nan
+        tif_zero_mask_path = processing_path + 'tif_mask_zero.tif'
+        roi.trans_tif2mask(tif_zero_mask_path, self.__preprocessed_paras[self.__sar_tif_name], np.nan)
 
         # 2、利用cover计算植被覆盖范围
         cover_mask_path = self.__workspace_processing_path + "SurfaceCover_mask.tif"
@@ -260,7 +261,7 @@ class LeafIndexMain:
         bare_land_mask_path = self.__workspace_processing_path + "bare_land_mask.tif"
         roi.combine_mask(bare_land_mask_path, ndvi_mask_path, cover_mask_path)
         roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_nan_mask_path)
-        # roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_zero_mask_path)
+        roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_zero_mask_path)
         roi.combine_mask(bare_land_mask_path, bare_land_mask_path, angle_nan_mask_path)
         logger.info('combine_mask success!')
 
@@ -291,6 +292,8 @@ class LeafIndexMain:
         v1 = 1
         vm_value = self.imageHandler.get_band_array(vm_file, 1)
         image_value = self.imageHandler.get_band_array(image_file, 1)
+        # image_value = np.power(10, (image_value / 10))
+        # image_value[np.isnan(image_value)] = 0
 
         # Filter.lee_filter_array(image_value, image_value, FILTER_SISE)
 
@@ -392,9 +395,8 @@ class LeafIndexMain:
                                                  incident_angle)  # ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数",'土壤含水量','入射角']
         lai_waiter_inc_sigma_list = combine_sample_attr(lai_water_inc_list,
                                                         sigma_tiff)  # ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数",'土壤含水量','入射角','后向散射系数']
-        # lai_waiter_inc_sigma_NDVI_list=combine_sample_attr(lai_waiter_inc_sigma_list,NDVI_tiff)    # ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数",'土壤含水量','入射角','后向散射系数','NDVI']
-        lai_waiter_inc_sigma_list = check_sample(
-            lai_waiter_inc_sigma_list)  # 清理样本 ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数",'土壤含水量','入射角','后向散射系数']
+        lai_waiter_inc_sigma_NDVI_list = combine_sample_attr(lai_waiter_inc_sigma_list, NDVI_tiff)
+        lai_waiter_inc_sigma_list = check_sample(lai_waiter_inc_sigma_NDVI_list)  # 清理样本 ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数",'土壤含水量','入射角','后向散射系数']
         # lai_waiter_inc_sigma_NDVI_list=check_sample(lai_waiter_inc_sigma_NDVI_list) # ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数",'土壤含水量','入射角','后向散射系数','NDVI']
         # 数据集筛选
         lai_waiter_inc_sigma_list_result = []
@@ -415,14 +417,14 @@ class LeafIndexMain:
         b = self.__processing_paras["B"]
         c = self.__processing_paras["C"]
         d = self.__processing_paras["D"]
-        params_X0 = [a, b, c, d, 0.771, -0.028]
+        params_X0 = [a, b, c, d]
         params_arr = train_WMCmodel(sample_train, params_X0, train_err_image_path, False)
         logging.info("模型初值:\t{}".format(str(params_X0)))
         logging.info("训练得到的模型系数:\t{}".format(str(params_arr)))
-        self.__processing_paras.update({"A": params_arr[0]})
-        self.__processing_paras.update({"B": params_arr[1]})
-        self.__processing_paras.update({"C": params_arr[2]})
-        self.__processing_paras.update({"D": params_arr[3]})
+        # self.__processing_paras.update({"A": params_arr[0]})
+        # self.__processing_paras.update({"B": params_arr[1]})
+        # self.__processing_paras.update({"C": params_arr[2]})
+        # self.__processing_paras.update({"D": params_arr[3]})
 
     def block_process(self,start):
         """
@@ -468,12 +470,13 @@ class LeafIndexMain:
             vm_path = self.__workspace_cai_SoilMoi_path + "soil_moisture" + suffix
             angle_path = self.__workspace_cai_localang_path + "LocalIncidenceAngle_preproed" + suffix
             v2_save_path = self.__workspace_Leafindex_path + "LeafAreaIndexProduct" + suffix
-            pl.append(pool.apply_async(self.cal_leaf_index, (vm_path, tif_path, angle_path, v2_save_path, num)))
+            # pl.append(pool.apply_async(self.cal_leaf_index, (vm_path, tif_path, angle_path, v2_save_path, num)))
+            self.cal_leaf_index(vm_path, tif_path, angle_path, v2_save_path, num)
             logger.info('total:%s, block:%s calculating leaf_index!', len(in_tif_paths), num)
             num = num + 1
 
-        pool.close()
-        pool.join()
+        # pool.close()
+        # pool.join()
         logger.info('all img cal LeafIndex success!')
         logger.info('progress bar: 80%')
         ref_tif_path = self.__ref_img_path

leafAreaIndex/LeafIndexXmlInfo.py

@@ -6,7 +6,8 @@
 @Date ：2021/10/19 14:39
 @Version ：1.0.0
 """
-
+import glob
+import os
 from xml.etree.ElementTree import ElementTree
 import shutil
 from tool.algorithm.image.ImageHandle import ImageHandler
@@ -232,3 +233,19 @@ class CreateStadardXmlFile:
         Parameter.find("CoverThresholdMax").text = str(self.set_threshold[3])
         tree.write(self.path, encoding="utf-8", xml_declaration=True)
 
+
+if __name__ == '__main__':
+    tif_dir = r'E:\SoilMoisture_cal\GF3BWENCANGtif'
+    out_dir = r'E:\SoilMoisture_cal\GF3_result'
+    tifs = list(glob.glob(os.path.join(tif_dir, "*.tif")))
+    for tif in tifs:
+        name = os.path.splitext(os.path.basename(tif))[0]
+        outPath = os.path.join(out_dir, name + '_lee.tif')
+
+        exe_path = r".\baseTool\x64\Release\SIMOrthoProgram.exe"
+        exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3} {4} {5}".format(exe_path, 12, tif,
+                                                                                           outPath, 7, 0.25)
+        print(exe_cmd)
+        print(os.system(exe_cmd))
+        print("==========================================================================")
+

leafAreaIndex/config.ini

@@ -4,7 +4,7 @@
 # 算法名称。修改临时工作区生成临时文件的名称，日志名称；
 exe_name = LeafAreaIndex
 # 开启调试模式则不删除临时工作区，True:开启调试，False:不开启调试
-debug = True
+debug = False
 tar = LAI
 productLevel = 5
 # 影像滤波尺寸。取值范围：大于1的奇数，默认值：7

leafAreaIndex/sample_process.py

@@ -1,7 +1,7 @@
 #
 #  样本处理的相关的库
 # 
-
+import logging
 from tool.algorithm.image.ImageHandle import ImageHandler
 import math
 import numpy as np
@@ -15,7 +15,7 @@ from scipy import interpolate
 from multiprocessing import pool
 # 常量声明区域
 imageHandler=ImageHandler()
-
+logger = logging.getLogger("mylog")
 
 # python 的函数类
 def read_sample_csv(csv_path):
@@ -202,3 +202,84 @@ def split_sample_list(sample_list,train_ratio):
     return  [sample_train,sample_test]
 
 
+def WMC(A,B,M,N,sample_soil,sample_inc,sample_sigma,sample_NDVI):
+    sigma_soil=M*sample_soil+N
+    theta=np.cos(sample_inc)
+    Atheta=A*theta
+    lnV=(sample_sigma-Atheta)/(sigma_soil-Atheta)
+    Mveg=-1*(theta/(2*B))*np.log(lnV)
+    # lai_ndvi=0*E*sample_NDVI+F
+    # return lai_ndvi
+    LAI=Mveg#(lai_ndvi+Mveg)/2
+    return LAI
+
+
+def WMCModel(param_arr,sample_lai,sample_soil,sample_inc,sample_sigma,sample_NDVI):
+    """ WMC模型 增加 归一化植被指数
+    Args:
+        param_arr (np.ndarray): 参数数组
+        sample_lai (double): 叶面积指数
+        sample_soil (double): 土壤含水量
+        sample_inc (double): 入射角（弧度值）
+        sample_sigma (double): 后向散射系数（线性值）
+    Returns:
+        double: 方程值
+    """
+    # 映射参数，方便修改模型
+    A,B,M,N=param_arr  # 在这里修改模型
+    LAI=WMC(A,B,M,N,sample_soil,sample_inc,sample_sigma,sample_NDVI)
+    result=LAI-sample_lai
+    return result
+
+
+def train_WMCmodel(lai_water_inc_sigma_list, params_X0, train_err_image_path, draw_flag=False):
+    """ 训练模型参数
+    Args:
+        lai_waiter_inc_sigma_list (list): 训练模型使用的样本呢
+    """
+
+    def f(X):
+        eqs = []
+        for lai_water_inc_sigma_item in lai_water_inc_sigma_list:
+            sample_lai = lai_water_inc_sigma_item[4]
+            sample_sigma = lai_water_inc_sigma_item[8]  # 5: csv_sigma, 8:tiff_sigma
+            sample_soil = lai_water_inc_sigma_item[6]
+            sample_inc = lai_water_inc_sigma_item[7]
+            FVC = lai_water_inc_sigma_item[8]
+            sample_NDVI = lai_water_inc_sigma_item[9]
+            eqs.append(WMCModel(X, sample_lai, sample_soil, sample_inc, sample_sigma, sample_NDVI))
+        return eqs
+
+    X0 = params_X0  # 初始值
+    logger.info(str(X0))
+    h = leastsq(f, X0)
+    logger.info(h[0], h[1])
+    err_f = f(h[0])
+    x_arr = [lai_waiter_inc_sigma_item[4] for lai_waiter_inc_sigma_item in lai_water_inc_sigma_list]
+    # 根据误差大小进行排序
+    logger.info("训练集：\n根据误差输出点序\n数量：{}\n点序\t误差值\t 样点信息".format(str(np.array(err_f).shape)))
+    for i in np.argsort(np.array(err_f)):
+        logger.info('{}\t{}\t{}'.format(i, err_f[i], str(lai_water_inc_sigma_list[i])))
+    logger.info("\n误差点序输出结束\n")
+
+    pred_lai = []
+    for i in range(len(err_f)):
+        pred_lai.append(err_f[i] + x_arr[i])
+
+    if draw_flag:
+        logger.info(err_f)
+        logger.info(np.where(np.abs(err_f) < 10))
+        from matplotlib import pyplot as plt
+        print(len(err_f), len(x_arr))
+        # plt.scatter(x_arr, pred_lai)
+        # plt.title("simulation Sigma and sample sigma")
+        # plt.xlabel("sample sigma")
+        # plt.ylabel("simulation sigma")
+        # plt.xlim(0, 10)
+        # plt.ylim(0, 10)
+        # plt.plot([0, 10], [0, 10])
+        # plt.savefig(train_err_image_path, dpi=600)
+        # plt.show()
+    return h[0]
+
+

soilMoistureTop/SoilMoisture.xml

@@ -36,7 +36,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\GF3_SYC_QPSI_040488_E110.7_N20.1_20240418_L1A_AHV_L10006923782-cal.tar.gz</ParaValue>
+		<ParaValue>F:\202306pj\GF3B_MYC_QPSI_008114_E121.6_N40.9_20230608_L1A_AHV_L10000196489-cal.tar.gz</ParaValue>
       </Parameter>
 	  <Parameter>
         <ParaName>Covering</ParaName>
@@ -45,7 +45,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\landCover_Glob30.tif</ParaValue>
+		<ParaValue>F:\202306pj\n51_40_2020lc030.tif</ParaValue>
       </Parameter>
       <Parameter>
         <ParaName>CoveringIDs</ParaName>
@@ -54,7 +54,7 @@
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>10;20;30;40;70;90</ParaValue>
+		<ParaValue>10;20;30;40;50</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -66,7 +66,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\NDVI\S2_NDVImed_SMC_GF3.tif</ParaValue>
+		<ParaValue>F:\202306pj\L9NDVI_GF3B_175394.tif</ParaValue>
       </Parameter>
       <Parameter>
         <ParaName>NDVIScope</ParaName>
@@ -100,7 +100,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\NDWI\S2_NDWImed_SMC_GF3.tif</ParaValue>
+		<ParaValue>F:\202306pj\ndwi\S2_NDWImed_panjin.tif</ParaValue>
       </Parameter>
       <Parameter>
         <ParaName>e1</ParaName>
@@ -109,7 +109,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>-12.772025319313864</ParaValue>
+		<ParaValue>-42.471051034087125</ParaValue>
         <MaxValue>100</MaxValue>
         <MinValue>-100</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -121,7 +121,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>11.555079721123748</ParaValue>
+		<ParaValue>-18.09398216319936</ParaValue>
         <MaxValue>100</MaxValue>
         <MinValue>-100</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -133,7 +133,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>28.605979879379152</ParaValue>
+		<ParaValue>-460667.08274463506</ParaValue>
         <MaxValue>9999</MaxValue>
         <MinValue>-9999</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -145,7 +145,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>-0.04303182290305167</ParaValue>
+		<ParaValue>0.16445643200517854</ParaValue>
         <MaxValue>9999</MaxValue>
         <MinValue>-9999</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -157,7 +157,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>-17.212613314078236</ParaValue>
+		<ParaValue>-604.983884544745</ParaValue>
         <MaxValue>9999</MaxValue>
         <MinValue>-9999</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -169,7 +169,7 @@
         <ParaType>Value</ParaType>
         <DataType>float</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>1.1965671296029239</ParaValue>
+		<ParaValue>1.498634203391113</ParaValue>
         <MaxValue>9999</MaxValue>
         <MinValue>-9999</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -183,7 +183,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\SoilMoisture\Output\GF3_SYC_QPSI_040488_E110.7_N20.1_20240418_L1A_AHV_L10006923782-cal-SMC.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SoilMoisture\Output\GF3B_MYC_QPSI_008114_E121.6_N40.9_20230608_L1A_AHV_L10000196489-cal-SMC.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

soilMoistureTop/SoilMoistureALg.py

@@ -137,8 +137,8 @@ class MoistureAlg:
             return False
 
 
-        mask_array = ImageHandler.get_band_array(mask_path, 1)
-        soil_dielectric[np.where(mask_array == 0)] = np.nan
+        # mask_array = ImageHandler.get_band_array(mask_path, 1)
+        # soil_dielectric[np.where(mask_array == 0)] = np.nan
 
         try:
             # Dobsen模型计算土壤水分
@@ -149,8 +149,8 @@ class MoistureAlg:
                 5.5 * np.power(10.0, -4) * np.power(soil_dielectric, 2) +  \
                 4.3 * np.power(10.0, -6) * np.power(soil_dielectric, 3)
             # 处理异常值
-            soil_moisture[where_tmp1_0] = 0
-            soil_moisture[where_tmp2_0] = 0
+            # soil_moisture[where_tmp1_0] = 0
+            # soil_moisture[where_tmp2_0] = 0
         except BaseException as msg:
             logger.error(msg)
             return False

soilMoistureTop/SoilMoistureMain.py

@@ -379,6 +379,7 @@ class MoistureMain:
 
         # data[data < soil_moisture_value_min] = soil_moisture_value_min
         # data[data > soil_moisture_value_max] = soil_moisture_value_max
+        data = data/100
         data[data < soil_moisture_value_min] = -9999
         data[data > soil_moisture_value_max] = -9999
         self.imageHandler.write_img(product_path, proj, geos, data, '-9999')
@@ -464,6 +465,7 @@ if __name__ == '__main__':
     except Exception:
         logger.exception('run-time error!')
     finally:
+
         main_handler.del_temp_workspace()
         pass
     end = datetime.datetime.now()

soilMoisture_geo_sar/SoilMoistureMain.py

@@ -465,7 +465,7 @@ class MoistureMain:
             for i in range(1, len(paths)):
                 SrcImageName=SrcImageName+";"+os.path.split(paths[i])[1].split('.tar.gz')[0]
         meta_xml_path = self.__product_dic+EXE_NAME+"Product.meta.xml"
-        CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process(SrcImageName)
+        CreateMetafile(self.__processing_paras[self.__sar_names_list[0] + '_Origin_META'], self.alg_xml_path, model_xml_path, meta_xml_path).process(SrcImageName)
 
         # 文件夹打包
 

soilMoisture_geo_sar/SoilMoisture_geo.xml

@@ -56,8 +56,8 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\202306hb\sar_img\GF3B_SYC_QPSI_008316_E116.0_N43.0_20230622_L1A_AHV_L10000202893-cal.tar.gz;
-          F:\202306hb\sar_img\GF3B_SYC_QPSI_008316_E116.1_N43.3_20230622_L1A_AHV_L10000202892-cal.tar.gz
+		<ParaValue>F:\MicroWorkspace\Micro\soil_geo\230224deqing\GF3_SAY_QPSI_008634_E102.3_N33.7_20180331_L1A_AHV_L10003096081-cal.tar.gz;
+          F:\MicroWorkspace\Micro\soil_geo\230224deqing\GF3_SAY_QPSI_009051_E102.3_N33.7_20180429_L1A_AHV_L10003153708-cal.tar.gz
         </ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
@@ -88,7 +88,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\202306hb\Landcover\50T_20220101-20230101.tif</ParaValue>
+		<ParaValue>F:\MicroWorkspace\Micro\soil_geo\cover\LandCover.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -118,7 +118,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\202306hb\NDVI\S2_202306_NDVI.tif</ParaValue>
+		<ParaValue>F:\MicroWorkspace\Micro\soil_geo\201806NDVI\201806_NDVImax.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 

soilSalinity-Train_predict/SoilSalinityPredict.xml

@@ -68,7 +68,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\landCover_Glob30.tif</ParaValue>
+		<ParaValue>F:\Tian-GF3-Wenchang\LandCover.tif</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -82,7 +82,7 @@
         <ParaType>File</ParaType>
         <DataType>zip</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>D:\micro\WorkSpace\SoilSalinityTrain\Output\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho-SSAA.zip</ParaValue>
+		<ParaValue>G:\算法备份\micro\WorkSpace\SoilSalinityTrain\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho-SSAA.zip</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -96,7 +96,7 @@
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>10;20;30;40;70;90</ParaValue>
+		<ParaValue>10;20;30;40;51;52;53;54;55;56;57;58;59;90</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

soilSalinity/SoilSalinity.xml

@@ -38,7 +38,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho.tar.gz</ParaValue>
+		<ParaValue>F:\2024xibei\GF3C_KSC_QPSI_008440_E86.0_N44.7_20231113_L1A_AHV_L10000215825-ortho.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -66,7 +66,7 @@
         <ParaType>File</ParaType>
         <DataType>csv</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>F:\Tian-GF3-Wenchang\soilSality.csv</ParaValue>
+        <ParaValue>F:\2024xibei\soilty.csv</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -80,7 +80,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\landCover.tif</ParaValue>
+		<ParaValue>F:\2024xibei\LandaCover.tif</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -94,7 +94,7 @@
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>empty</ParaValue>
+		<ParaValue>90</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -108,7 +108,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\NDVI\S2_NDVImed_SMC_GF3.tif</ParaValue>
+		<ParaValue>F:\2024xibei\S2_NDVImed2.tif</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -138,7 +138,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\SoilSalinity\Output\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho-SSAA.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SoilSalinity\Output\GF3C_KSC_QPSI_008440_E86.0_N44.7_20231113_L1A_AHV_L10000215825-ortho-SSAA.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

soilSalinity/SoilSalinityMain.py

@@ -10,6 +10,7 @@
 [修改序列]   [修改日期]    [修改者]     [修改内容]
    1       2022-6-27     石海军      1.增加配置文件config.ini; 2.内部处理使用地理坐标系(4326)；
 """
+import glob
 import logging
 import shutil
 
@@ -122,6 +123,7 @@ class SalinityMain:
         # 元文件字典
         # para_dic.update(InitPara.get_meta_dic(InitPara.get_meta_paths(file_dir, name), name))
         para_dic.update(InitPara.get_meta_dic_new(InitPara.get_meta_paths(file_dir, name), name))
+        para_dic.update({'incXML': InitPara.get_incidence_xml_paths(file_dir, name)[0]})
         # tif路径字典
         para_dic.update(InitPara.get_polarization_mode(InitPara.get_tif_paths(file_dir, name)))
         parameter_path = os.path.join(file_dir, "orth_para.txt")
@@ -220,7 +222,13 @@ class SalinityMain:
         polarization = ['HH', 'HV', 'VH', 'VV']
         calibration = Calibration.get_Calibration_coefficient(self.__processing_paras['Origin_META'], polarization)
         tif_path = atp.calibration(calibration, in_ahv_dir=self.__workspace_preprocessed_path)
-        atp.ahv_to_polsarpro_t3_soil(t3_path, tif_path)
+
+        inc_path = os.path.join(self.__workspace_processing_path, 'inc_angle.tif')
+        in_tif_paths = list(glob.glob(os.path.join(ahv_path, '*.tif')))
+        rows = ImageHandler.get_img_height(in_tif_paths[0])
+        ImageHandler.get_inc_angle(self.__processing_paras['incXML'], rows, 1, inc_path)
+
+        atp.ahv_to_polsarpro_t3_soil(t3_path, inc_path, tif_path)
 
         logger.info('ahv transform to polsarpro T3 matrix success!')
         logger.info('progress bar: 20%')

surfaceRoughness_oh2004/SurfaceRoughness.xml

@@ -38,7 +38,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho.tar.gz</ParaValue>
+		<ParaValue>F:\2024xibei\GF3B_KSC_QPSI_010328_E86.0_N44.7_20231109_L1A_AHV_L10000262135-ortho.tar.gz</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -68,7 +68,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\landCover_Glob30.tif</ParaValue>
+		<ParaValue>F:\2024xibei\LandaCover.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -83,7 +83,7 @@
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>10;20;30;40;70;90</ParaValue>
+		<ParaValue>10;90</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>UploadInput</Control> 
@@ -98,7 +98,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\Tian-GF3-Wenchang\NDVI\S2_NDVImed_SMC_GF3.tif</ParaValue>
+		<ParaValue>F:\2024xibei\S2_NDVImed2.tif</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -130,7 +130,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\SurfaceRoughness\Output\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho-SR.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SurfaceRoughness\Output\GF3B_KSC_QPSI_010328_E86.0_N44.7_20231109_L1A_AHV_L10000262135-ortho-SR.tar.gz</ParaValue>
       </Parameter>
     </Outputs>
   </AlgCompt>

surfaceRoughness_oh2004/SurfaceRoughnessTool.py

@@ -43,7 +43,13 @@ class SoilMoistureTool:
         polarization = ['HH', 'HV', 'VH', 'VV']
         calibration = Calibration.get_Calibration_coefficient(self.xml_path, polarization)
         tif_path = atp.calibration(calibration, in_ahv_dir=self.__workspace_processing_path)
-        atp.ahv_to_polsarpro_t3_soil(t3_path, tif_path)
+
+        inc_path = self.incidence_file
+        # in_tif_paths = list(glob.glob(os.path.join(t3_path, '*.tif')))
+        # rows = ImageHandler.get_img_height(in_tif_paths[0])
+        # ImageHandler.get_inc_angle(self.xml_path, rows, 1, inc_path)
+
+        atp.ahv_to_polsarpro_t3_soil(t3_path, inc_path, tif_path)
 
         # Lee滤波
         leeFilter = LeeRefinedFilterT3()

tool/algorithm/algtools/MetaDataHandler.py

@@ -124,21 +124,25 @@ class Calibration:
                 kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
                 data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
                 calibration[0] = math.sqrt(data_value)
+                print("HH", calibration[0])
             if i == 'HV':
                 quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
                 kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
                 data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
                 calibration[1] = math.sqrt(data_value)
+                print("HV", calibration[1])
             if i == 'VH':
                 quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
                 kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
                 data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
                 calibration[2] = math.sqrt(data_value)
+                print("VH", calibration[2])
             if i == 'VV':
                 quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
                 kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
                 data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
                 calibration[3] = math.sqrt(data_value)
+                print("VV", calibration[3])
         return calibration
 
 

tool/algorithm/algtools/PreProcess.py

@@ -27,7 +27,7 @@ from tool.algorithm.image.ImageHandle import ImageHandler
 import logging
 logger = logging.getLogger("mylog")
 #
-# os.environ['PROJ_LIB'] = os.getcwd()
+os.environ['PROJ_LIB'] = os.getcwd()
 
 
 
@@ -207,7 +207,7 @@ class PreProcess:
     def trans_epsg4326(out_path, in_path):
         OutTile = gdal.Warp(out_path, in_path,
                             dstSRS='EPSG:4326',
-                            resampleAlg=gdalconst.GRA_Bilinear
+                            resampleAlg=gdalconst.GRA_NearestNeighbour
                             )
         OutTile = None
         return True
@@ -414,7 +414,7 @@ class PreProcess:
 
         input_dataset = gdal.Open(input_path)
 
-        gdal.Warp(output_path, input_dataset, format='GTiff', outputBounds=box, cutlineDSName=shp_path, dstNodata=-9999)
+        gdal.Warp(output_path, input_dataset, format='GTiff', outputBounds=box, cutlineDSName=shp_path, dstNodata=0, resampleAlg='nearest')
         # cutlineWhere="FIELD =  ‘whatever’",
         # optionally you can filter your cutline (shapefile) based on attribute values
         # select the no data value you like
@@ -582,8 +582,18 @@ class PreProcess:
 
 
 if __name__ == '__main__':
-    fn = r"F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1B_v_h_L10006928143-ortho.tif"
-    out = r"F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1B_v_h_L10006928143-ortho.tif"
-    PreProcess.resample_by_gdal(fn, out)
+    # fn = r"F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1B_v_h_L10006928143-ortho.tif"
+    # out = r"F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1B_v_h_L10006928143-ortho.tif"
+    # PreProcess.resample_by_gdal(fn, out)
+    inTif = r"D:\micro\WorkSpace\VegetationPhenology\Temporary\preprocessing\20240427_Covering_cut.tif"
+    outTif = r"D:\micro\WorkSpace\VegetationPhenology\Temporary\preprocessing\20240427_Covering_cut_re.tif"
+    shpPath = r"D:\micro\test\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho-SSAA.tif"
+    tarTif = r"D:\micro\VP_class\band_merged.tif"
+    # PreProcess.resampling_by_scale(inTif, outTif, tarTif)
+
+    print(10 * np.log10(0))
+
+
+    # PreProcess.cut_img(outTif, inTif, shpPath)
 
 

tool/algorithm/algtools/ROIAlg.py

@@ -230,10 +230,13 @@ class ROIAlg:
         return True
 
 if __name__ == '__main__':
-    dir = r'G:\MicroWorkspace\C-SAR\SoilMoisture\Temporary\processing/'
-    out_tif_path = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\SurfaceRoughnessProduct_test.tif'
-    in_tif_path = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\SurfaceRoughnessProduct_geo.tif'
-    mask_path = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\processing\roi\ndvi_mask.tif'
-    background_value = 0
-    ROIAlg.cal_roi(out_tif_path, in_tif_path, mask_path, background_value)
+    # dir = r'G:\MicroWorkspace\C-SAR\SoilMoisture\Temporary\processing/'
+    out_tif_path = r"D:\BaiduNetdiskDownload\veg_landcover_re_mask.tif"
+    in_tif_path = r"D:\BaiduNetdiskDownload\veg_landcover_re.tif"
+    # mask_path = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\processing\roi\ndvi_mask.tif'
+    # background_value = 0
+    # ROIAlg.cal_roi(out_tif_path, in_tif_path, mask_path, background_value)
+    cover_id_list = [10, 40, 90]
+
+    ROIAlg.trans_cover2mask(out_tif_path, in_tif_path, cover_id_list)
     pass

tool/algorithm/algtools/filter/lee_Filter.py

@@ -8,6 +8,7 @@
 @Date:2021/8/30 8:42
 @Version:1.0.0
 """
+import glob
 
 import numpy as np
 import math
@@ -296,6 +297,20 @@ if __name__ == '__main__':
     # f = Filter()
     # f.lee_filter(path,path,3)
     #示例2：
+    tif_dir = r''
+    out_dir = r''
+    tifs = list(glob.glob(os.path.join(tif_dir, "*.tif")))
+    for tif in tifs:
+        name = os.path.splitext(os.path.basename(tif))[0]
+        outPath = os.path.join(out_dir, name)
+
+        exe_path = r".\baseTool\x64\Release\SIMOrthoProgram.exe"
+        exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3} {4} {5}".format(exe_path, 12, tif,
+                                                                                           outPath, 7, 0.25)
+        print(exe_cmd)
+        print(os.system(exe_cmd))
+        print("==========================================================================")
+
 
     f = Filter()
     f.lee_filter_block_multiprocess('I:\preprocessed\HH.tif','I:\preprocessed\HHf.tif')

tool/algorithm/image/ImageHandle.py

@@ -928,7 +928,13 @@ if __name__ == '__main__':
 #     s = ImageHandler().band_merge(path, path2, path3)
 #     print(s)
 #     pass
-    fn = r"D:\BaiduNetdiskDownload\植被物候\chen_features_warp.tif"
-    outP = r'D:\BaiduNetdiskDownload\植被物候\chen_features_warp_LWZ.tif'
-    im_proj, im_geotrans, im_arr = ImageHandler.read_img(fn)
-    ImageHandler.write_img(outP, im_proj, im_geotrans, im_arr)
+    fileDir = r'D:\micro\WorkSpace\ortho\Temporary\VH_db'
+    outDir = r'D:\micro\WorkSpace\ortho\Temporary\VH_db\test'
+    files = os.listdir(fileDir)
+    for file in files:
+        tifFile = os.path.join(fileDir, file)
+        outFile = os.path.join(outDir, file)
+        im_proj, im_geotrans, im_arr = ImageHandler.read_img(tifFile)
+        im_arr[np.isnan(im_arr)] = 0
+        im_arr[np.isinf(im_arr)] = 0
+        ImageHandler.write_img(outFile, im_proj, im_geotrans, im_arr, '0')

tool/algorithm/ml/machineLearning.py

@@ -180,7 +180,7 @@ class MachineLeaning:
             # features_array = ImageHandler.get_data(path)
             band = ImageHandler.get_bands(path)
             if band == 1:
-                features_array = np.zeros((1, 1024, 1024), dtype=float)
+                features_array = np.zeros((1, block_size, block_size), dtype=float)
                 feature_array = ImageHandler.get_data(path)
                 features_array[0, :, :] = feature_array
             else:

tool/algorithm/polsarpro/AHVToPolsarpro.py

@@ -72,13 +72,16 @@ class AHVToPolsarpro:
         return s11, s12, s21, s22
 
     @staticmethod
-    def __ahv_to_s2_soil(ahv_dir):
+    def __ahv_to_s2_soil(ahv_dir, inc_angle):
         """
         全极化影像转S2矩阵
         :param ahv_dir: 全极化影像文件夹路径
         :return: 极化散射矩阵S2
         """
         global s11
+        _, _, inc_arr = ImageHandler().read_img(inc_angle)
+        rad_arr = np.radians(inc_arr)
+        sin_arr = np.sin(rad_arr)
         in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tif')))
         in_tif_paths1 = list(glob.glob(os.path.join(ahv_dir, '*.tiff')))
         in_tif_paths += in_tif_paths1
@@ -93,22 +96,22 @@ class AHVToPolsarpro:
             if 'HH' in os.path.basename(in_tif_path):
                 data_real = data[0, :, :]
                 data_imag = data[1, :, :]
-                s11 = data_real + 1j * data_imag
+                s11 = data_real + 1j * data_imag / sin_arr
                 flag_list[0] = 1
             elif 'HV' in os.path.basename(in_tif_path):
                 data_real = data[0, :, :]
                 data_imag = data[1, :, :]
-                s12 = data_real + 1j * data_imag
+                s12 = data_real + 1j * data_imag / sin_arr
                 flag_list[1] = 1
             elif 'VH' in os.path.basename(in_tif_path):
                 data_real = data[0, :, :]
                 data_imag = data[1, :, :]
-                s21 = data_real + 1j * data_imag
+                s21 = data_real + 1j * data_imag / sin_arr
                 flag_list[2] = 1
             elif 'VV' in os.path.basename(in_tif_path):
                 data_real = data[0, :, :]
                 data_imag = data[1, :, :]
-                s22 = data_real + 1j * data_imag
+                s22 = data_real + 1j * data_imag / sin_arr
                 flag_list[3] = 1
             else:
                 continue
@@ -380,10 +383,10 @@ class AHVToPolsarpro:
         self.__t3_to_polsarpro_t3(out_file_dir, t11, t12, t13, t22, t23, t33)
 
 
-    def ahv_to_polsarpro_t3_soil(self, out_file_dir, in_ahv_dir=''):
+    def ahv_to_polsarpro_t3_soil(self, out_file_dir, inc_path, in_ahv_dir=''):
 
         if self._hh_hv_vh_vv_path_list == [] :
-            s11, s12, s21, s22 = self.__ahv_to_s2_soil(in_ahv_dir)
+            s11, s12, s21, s22 = self.__ahv_to_s2_soil(in_ahv_dir, inc_path)
         else:
             s11, s12, s21, s22 = self.__ahv_to_s2_list_2(self._hh_hv_vh_vv_path_list)
 

tool/algorithm/polsarpro/pspSurfaceInversion.py

@@ -343,8 +343,8 @@ class SurfaceInversionOh2004:
         Sub_Nlig = row
         Sub_Ncol = col
         dataFormat = 'T3'
-        threshold_mv = 1.0
-        threshold_s = 7.0
+        threshold_mv = 0.15
+        threshold_s = 2.5
 
         para_list = [
             exePath,

tool/algorithm/transforml1a/transHandle.py

@@ -989,7 +989,11 @@ class TransImgL1A_ori:
         source_def = geometry.SwathDefinition(lons=lon_data, lats=lat_data)
         lalo_step = [pixel_delta_x, -pixel_delta_y]
         radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
-        geo_produc = pr.bilinear.NumpyBilinearResampler(source_def, target_def, neighbours=32, radius_of_influence=radius, epsilon=0).resample(l1a_produc)
+        # geo_produc = pr.bilinear.NumpyBilinearResampler(source_def, target_def, neighbours=32, radius_of_influence=radius, epsilon=0).resample(l1a_produc)
+        geo_produc = pr.bilinear.resample_bilinear(l1a_produc, source_def, target_def,
+                                                   radius=radius, neighbours=32,
+                                                   nprocs=80, fill_value=0,
+                                                   epsilon=0)
         # geo_produc = pr.bilinear.resample_bilinear(l1a_produc, source_def, target_def,
         #                                            radius=radius, neighbours=32,
         #                                            nprocs=8, fill_value=np.nan,
@@ -1064,9 +1068,9 @@ if __name__ == '__main__':
     #
     # tr = TransImgL1A(ori_sim_path,roi_Extend)
     # tr.l1a_2_geo("I:/cut.tif", "I:/salinity.tif", "I:/salinity_geo2.tif")
-    ori_sim = r"D:\micro\WorkSpace\AtmosphericDelay\Temporary\cut\ori_sim.tif"
-    product_tif = r"D:\micro\LWork\AtmosphericDelay\test\pro_m_filt_fine_ERA5.tif"
-    result = r"D:\micro\LWork\AtmosphericDelay\test\pro_m_filt_fine_ERA5_geo.tif"
+    ori_sim = r"D:\micro\LWork\AtmosphericDelay\Temporary\cut\ori_sim.tif"
+    product_tif = r"D:\micro\LWork\AtmosphericDelay\Temporary\apsWork\master_ztd\dlosout.tiff"
+    result = r"D:\micro\LWork\AtmosphericDelay\Temporary\apsWork\master_ztd\dlosout_geo.tiff"
     method = 'linear'
     scopes = ImageHandle.ImageHandler().get_scopes(ori_sim)
     # """

vegetationPhenology/SurfaceRoughnessMain.spec

@@ -1,33 +0,0 @@
-# -*- mode: python ; coding: utf-8 -*-
-
-block_cipher = None
-
-
-a = Analysis(['SurfaceRoughnessMain.py'],
-             pathex=['D:\\Project\\microproduct\\vegetationPhenology'],
-             binaries=[],
-             datas=[('E:/soft/Anaconda/envs/micro/Lib/site-packages/dask/dask.yaml', './dask'), ('E:/soft/Anaconda/envs/micro/Lib/site-packages/distributed/distributed.yaml', './distributed')],
-             hiddenimports=[],
-             hookspath=[],
-             runtime_hooks=[],
-             excludes=[],
-             win_no_prefer_redirects=False,
-             win_private_assemblies=False,
-             cipher=block_cipher,
-             noarchive=False)
-pyz = PYZ(a.pure, a.zipped_data,
-             cipher=block_cipher)
-exe = EXE(pyz,
-          a.scripts,
-          a.binaries,
-          a.zipfiles,
-          a.datas,
-          [],
-          name='SurfaceRoughnessMain',
-          debug=False,
-          bootloader_ignore_signals=False,
-          strip=False,
-          upx=True,
-          upx_exclude=[],
-          runtime_tmpdir=None,
-          console=True )

vegetationPhenology/VegetationPhenology.xml

@@ -1,143 +0,0 @@
-<?xml version='1.0' encoding='utf-8'?>
-<Root>
-  <TaskID>CSAR_202107275419_0001-0</TaskID>
-  <WorkSpace>D:\micro\WorkSpace\</WorkSpace>
-  <AlgCompt>
-    <DataTransModel>File</DataTransModel>
-    <Artificial>ElementAlg</Artificial>
-    <AlgorithmName>VegetationPhenology-C-SAR-V2.1</AlgorithmName>
-    <DllName>VegetationPhenology-C-SAR-V2.1.exe</DllName>
-    <ChsName>植被物候产品</ChsName>
-    <AlgorithmDesc>微波卫星3-5级产品生产模型</AlgorithmDesc>
-    <Version>2.1</Version>
-    <AlgorithmClass>植被类产品_植被物候产品</AlgorithmClass>
-    <AlgorithmLevel>5</AlgorithmLevel>
-		<AlgoirthmID>VegetationPhenology_中科卫星应用德清研究院_2.1</AlgoirthmID>
-    <Author>中科卫星应用德清研究院</Author>
-    <Type>景-算法</Type>
-    <jdkVersion>1.8</jdkVersion>
-    <algDevlanguage>python</algDevlanguage>
-	<Environment>
-      <IsCluster>0</IsCluster>
-      <ClusterNum>0</ClusterNum>
-      <OperatingSystem>Windows10</OperatingSystem>F
-      <CPU>双核</CPU>
-      <Memory>4GB</Memory>
-      <Storage>2GB</Storage>
-      <NetworkCard>无需求</NetworkCard>
-      <Bandwidth>无需求</Bandwidth>
-      <GPU>无需求</GPU>
-    </Environment>
-		<Utility Satellite="GF3C" Sensor="MSS" Resolution="1" />
-    <Inputs ParameterNum="6">
-      <Parameter>
-        <ParaName>AHVS</ParaName>
-        <ParaChsName>全极化影像集</ParaChsName>
-        <Description>多景全极化影像，每一个物候期至少对应一景影像，多个路径用';'间隔</Description>
-        <ParaType>File</ParaType>
-        <DataType>tar.gz</DataType>
-		<ParaSource>Man</ParaSource>
-		<ParaValue>
-          F:\VegetationPhenology-likun\rusuoces\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764-ortho.tar.gz
-        </ParaValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <MinValue>DEFAULT</MinValue>
-        <OptionValue>DEFAULT</OptionValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-	   <Parameter>
-        <ParaName>MeasuredData</ParaName>
-        <ParaChsName>植被物候类型数据</ParaChsName>
-        <Description>植被物候类型数据</Description>
-        <ParaType>File</ParaType>
-        <DataType>csv</DataType>
-		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\BaiduNetdiskDownload\植被物候\vegetationRoi_new.csv</ParaValue>
-         <MaxValue>DEFAULT</MaxValue>
-        <MinValue>DEFAULT</MinValue>
-        <OptionValue>DEFAULT</OptionValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-      <Parameter>
-        <ParaName>Covering</ParaName>
-        <ParaChsName>地表覆盖类型数据</ParaChsName>
-        <Description>经过地理定标（WGS84）的地表覆盖类型数据</Description>
-        <ParaType>File</ParaType>
-        <DataType>tif</DataType>
-		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\VegetationPhenology-likun\rusuoces\N47_25_2020LC030\n47_25_2020lc030.tif</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>False</EnMultipleChoice>
-        <Control>File</Control>
-        <InputType>Aux</InputType>
-        <InputNum>1</InputNum>
-        <DateFrom>Aux</DateFrom>
-      </Parameter>
-       <Parameter>
-        <ParaName>CoveringIDs</ParaName>
-        <ParaChsName>地表覆盖类型数据的类别id</ParaChsName>
-        <Description>地表覆盖类型数据中植被区域、裸土区域的类别id,多个id使用“;”分割。示例：GlobeLand30数据的cover_roi_ids = 10;20;30;40;50;70;71;72;83;74;90</Description>
-        <ParaType>Value</ParaType>
-        <DataType>string</DataType>
-		<ParaSource>Man</ParaSource>
-		<ParaValue>10</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>False</EnMultipleChoice>
-        <Control>UploadInput</Control>
-        <InputType>Aux</InputType>
-        <InputNum>0</InputNum>
-        <DateFrom>Aux</DateFrom>
-      </Parameter>
-      <Parameter>
-	    <ParaName>box</ParaName>
-        <ParaChsName>经纬度包围盒</ParaChsName>
-        <Description>经纬度包围盒SNWE。例子31.527611633687169;31.397153571335469;118.94152;119.02 new不填写则使用影像相交区域作为包围盒38.171;38.1878;114.36567;114.39168</Description>
-        <ParaType>Value</ParaType>
-        <DataType>string</DataType>
-		<ParaSource>Man</ParaSource>
-		<ParaValue>empty</ParaValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <MinValue>DEFAULT</MinValue>
-        <OptionValue>DEFAULT</OptionValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-      <Parameter>
-        <ParaName>FeatureCombination</ParaName>
-        <ParaChsName>极化特征组合</ParaChsName>
-        <Description>可选极化特征组合一、共10种特征（编号依次为0-9）
-		Freeman：表面散射p_s(0)、偶次散射p_d(1)、体散射p_v(2);
-		Yamaguchi：表面散射f_s(3)、二次散射f_d(4)、体散射f_v(5)、螺旋体散射f_h(6);
-		Cloude-Pottier：分解散射熵H(7)、反熵A(8)、平均散射角α(9)</Description>
-        <ParaType>Value</ParaType>
-        <DataType>string</DataType>
-		<ParaSource>Man</ParaSource>
-		<ParaValue>0,1,2,3,4,5,6,7,8,9</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>True</EnMultipleChoice>
-        <Control>UploadInput</Control>
-        <InputType>Aux</InputType>
-        <InputNum>0</InputNum>
-        <DateFrom>Aux</DateFrom>
-      </Parameter>
-    </Inputs>
-    <Outputs ParameterNum="1">
-      <Parameter>
-        <ParaName>VegetationPhenologyProduct</ParaName>
-        <ParaChsName>植被物候产品</ParaChsName>
-        <Description>植被物候产品</Description>
-        <ParaType>File</ParaType>
-        <DataType>tar.gz</DataType>
-		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\VegetationPhenology\Output\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764-ortho-VP.tar.gz</ParaValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <MinValue>DEFAULT</MinValue>
-        <OptionValue>DEFAULT</OptionValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-    </Outputs>
-  </AlgCompt>
-</Root>

vegetationPhenology/VegetationPhenologyAuxData.py

@@ -283,7 +283,8 @@ class PhenoloyMeasCsv_geo:
         n = 1
         train_data_list = []
         for data in meas_data:
-
+            if len(data)==0:
+                continue
             for d in data:
                 if d == '':
                     raise Exception('there are empty data!', data)
@@ -319,22 +320,22 @@ class PhenoloyMeasCsv_geo:
                             train_data[3] = train_data[3] + self.__render(poly)
                     if train_data[3] == [] :
                         pass
-        #                 raise Exception('there are empty data!', train_data)
-        #
-        # if len(train_data_list) <= 1:
-        #     raise Exception('there is only one label type!', train_data_list)
-        #
-        # num_list = []
-        # for train_data in train_data_list:
-        #     if not len(train_data[3]) == 0:
-        #         num_list.append(len(train_data[3]))
-        # max_num = np.min(num_list)
-        # for train_data in train_data_list:
-        #     logger.info(str(train_data[0]) + "," + str(train_data[2]) +"," + "num:" + str(len(train_data[3])))
-        #     # max_num = self.__max_tran__num_per_class
-        #     logger.info("max number =" + str(max_num) +", random select"+str(max_num)+" point as train data!")
-        #     if(len(train_data[3]) > max_num):
-        #         train_data[3] = random.sample(train_data[3], max_num)
+                        raise Exception('there are empty data!', train_data)
+
+        if len(train_data_list) <= 1:
+            raise Exception('there is only one label type!', train_data_list)
+
+        num_list = []
+        for train_data in train_data_list:
+            if not len(train_data[3]) == 0:
+                num_list.append(len(train_data[3]))
+        max_num = np.min(num_list)
+        for train_data in train_data_list:
+            logger.info(str(train_data[0]) + "," + str(train_data[2]) +"," + "num:" + str(len(train_data[3])))
+            # max_num = self.__max_tran__num_per_class
+            logger.info("max number =" + str(max_num) +", random select"+str(max_num)+" point as train data!")
+            if(len(train_data[3]) > max_num):
+                train_data[3] = random.sample(train_data[3], max_num)
 
 
         return train_data_list
@@ -381,7 +382,7 @@ class PhenoloyMeasCsv_geo:
         :return pointList: 保存多边形的list
         """
         pointList = []
-        strContent = roiStr.replace("POLYGON", "")
+        strContent = roiStr.replace("POLYGON", "").replace(", ", ",")
         # 解析轮廓字符串为二维数组
         bracketsList = []
         strTemp = ''

vegetationPhenology/VegetationPhenologyMain.py

@@ -410,7 +410,7 @@ class PhenologyMain:
         logger.info("generate train and test set success!")
         logger.info('progress bar: 30%')
 
-        optimal_X_train, optimal_Y_train, optimal_feature = ml.sel_optimal_feature(X_train, Y_train, total_name_list,important_threshold=0.0, correlation_threshold=20)
+        optimal_X_train, optimal_Y_train, optimal_feature = ml.sel_optimal_feature(X_train, Y_train, total_name_list,important_threshold=0.07, correlation_threshold=0.85)
 
         # RF
         clf = ml.trainRF(optimal_X_train, optimal_Y_train)
@@ -629,6 +629,7 @@ if __name__ == '__main__':
         logger.exception("run-time error!")
     finally:
         main_handler.del_temp_workspace()
+        pass
 
     end = datetime.datetime.now()
     msg = 'running use time: %s ' % (end - start)

vegetationPhenology/VegetationPhenology_C_SAR_V3.xml

@@ -1,184 +0,0 @@
-<?xml version='1.0' encoding='utf-8'?>
-<Root>
-  <TaskID>CSAR_202107275419_0001-0</TaskID>
-  <WorkSpace>D:\micro\WorkSpace\</WorkSpace>
-  <AlgCompt>
-    <DataTransModel>File</DataTransModel>
-    <Artificial>ElementAlg</Artificial>
-    <AlgorithmName>VegetationPhenology_C_SAR_V3</AlgorithmName>
-    <DllName>VegetationPhenology_C_SAR_V3.exe</DllName>
-    <ChsName>植被物候产品</ChsName>
-    <AlgorithmDesc>微波卫星3-5级产品生产模型</AlgorithmDesc>
-    <Version>3</Version>
-    <AlgorithmClass>植被类产品_植被物候产品</AlgorithmClass>
-    <AlgorithmAlias>VegetationPhenology_C_SAR_V3</AlgorithmAlias>
-    <AlgorithmLevel>5</AlgorithmLevel>
-    <AlgoirthmID>VegetationPhenology_CSAR_中科卫星应用德清研究院_3</AlgoirthmID>
-    <Author>中科卫星应用德清研究院</Author>
-    <Type>景-算法</Type>
-    <jdkVersion>1.8</jdkVersion>
-    <algDevlanguage>python</algDevlanguage>
-    <Environment>
-      <IsCluster>0</IsCluster>
-      <ClusterNum>0</ClusterNum>
-      <OperatingSystem>Windows10</OperatingSystem>
-      <CPU>双核</CPU>
-      <Memory>32GB</Memory>
-      <Storage>64GB</Storage>
-      <NetworkCard>无需求</NetworkCard>
-      <Bandwidth>
-      无需求</Bandwidth>
-      <GPU>无需求</GPU>
-    </Environment>
-    <Utility Satellite="GF3BC" Sensor="MSS" Resolution="1" />
-    <Inputs ParameterNum="6">
-      <Parameter>
-        <ParaName>AHVS</ParaName>
-        <ParaChsName>全极化影像集</ParaChsName>
-        <Description>多景全极化影像，每一个物候期至少对应一景影像，多个路径用';'间隔</Description>
-        <ParaType>File</ParaType>
-        <DataType>tar.gz</DataType>
-        <ParaSource>Cal</ParaSource>
-        <ParaValue>
-          E:\MicroWorkspace\GF3A_nanjing\input-ortho\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho.tar.gz;
-          E:\MicroWorkspace\GF3A_nanjing\input-ortho\GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho.tar.gz</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>False</EnMultipleChoice>
-        <Control>File</Control>
-        <InputType>production</InputType>
-        <InputNum>2</InputNum>
-        <OptionValue>DEFAULT</OptionValue>
-        <MinValue>DEFAULT</MinValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-        <DateFrom>Ortho_CSAR_中科卫星应用德清研究院_3</DateFrom>
-      </Parameter>
-      <Parameter>
-        <ParaName>MeasuredData</ParaName>
-        <ParaChsName>植被物候类型数据</ParaChsName>
-        <Description>植被物候类型数据</Description>
-        <ParaType>File</ParaType>
-        <DataType>csv</DataType>
-        <ParaSource>Cal</ParaSource>
-        <ParaValue>
-          E:\MicroWorkspace\GF3A_nanjing\VegetationPhenologyMeasureData_E118.9_N31.4_37_5.csv</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>False</EnMultipleChoice>
-        <Control>UploadTable</Control>
-        <InputType>production</InputType>
-        <InputNum>1</InputNum>
-        <DateFrom>AUX</DateFrom>
-        <OptionValue>DEFAULT</OptionValue>
-        <MinValue>DEFAULT</MinValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-      <Parameter>
-        <ParaName>Covering</ParaName>
-        <ParaChsName>地表覆盖类型数据</ParaChsName>
-        <Description>经过地理定标（WGS84）的地表覆盖类型数据</Description>
-        <ParaType>File</ParaType>
-        <DataType>tif</DataType>
-        <OptionValue>DEFAULT</OptionValue>
-        <MinValue>DEFAULT</MinValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\202306pj\n51_40_2020lc030.tif</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>False</EnMultipleChoice>
-        <Control>UploadTable</Control>
-        <InputType>Aux</InputType>
-        <InputNum>1</InputNum>
-        <DateFrom>Aux</DateFrom>
-      </Parameter>
-      <Parameter>
-        <ParaName>CoveringIDs</ParaName>
-        <ParaChsName>地表覆盖类型数据的类别id</ParaChsName>
-        <Description>地表覆盖类型数据中植被区域、裸土区域的类别id,多个id使用“;”分割。示例：GlobeLand30数据的cover_roi_ids =
-          10;20;30;40;50;70;71;72;83;74;90</Description>
-        <ParaType>Value</ParaType>
-        <DataType>string</DataType>
-        <OptionValue>DEFAULT</OptionValue>
-        <MinValue>DEFAULT</MinValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <ParaSource>Man</ParaSource>
-        <ParaValue>10;20;30;40;50;60;70;71;72;80;83;74;90;100</ParaValue>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>False</EnMultipleChoice>
-        <Control>UploadInput</Control>
-        <InputType>Aux</InputType>
-        <InputNum>0</InputNum>
-        <DateFrom>Aux</DateFrom>
-      </Parameter>
-      <Parameter>
-        <ParaName>box</ParaName>
-        <ParaChsName>经纬度包围盒</ParaChsName>
-        <Description>经纬度包围盒SNWE。例子31.527611633687169;31.397153571335469;118.94152;119.02
-          new不填写则使用影像相交区域作为包围盒38.171;38.1878;114.36567;114.39168</Description>
-        <ParaType>Value</ParaType>
-        <DataType>string</DataType>
-        <ParaSource>Man</ParaSource>
-        <OptionValue>DEFAULT</OptionValue>
-        <MinValue>DEFAULT</MinValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <ParaValue>empty</ParaValue>
-        <EnModification>True</EnModification> 
-        <EnMultipleChoice>False</EnMultipleChoice> 
-        <Control>UploadInput</Control> 
-        <InputType>Aux</InputType>
-        <InputNum>0</InputNum>
-        <DateFrom>Aux</DateFrom>
-        <MaxValue>DEFAULT</MaxValue>
-        <MinValue>DEFAULT</MinValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-      <Parameter>
-        <ParaName>FeatureCombination</ParaName>
-        <ParaChsName>极化特征组合</ParaChsName>
-        <Description>可选极化特征组合一、共10种特征（编号依次为0-9）
-		Freeman：表面散射p_s(0)、偶次散射p_d(1)、体散射p_v(2);
-		Yamaguchi：表面散射f_s(3)、二次散射f_d(4)、体散射f_v(5)、螺旋体散射f_h(6);
-		Cloude-Pottier：分解散射熵H(7)、反熵A(8)、平均散射角α(9)</Description>
-        <ParaType>Value</ParaType>
-        <DataType>string</DataType>
-        <OptionValue>DEFAULT</OptionValue>
-        <MinValue>DEFAULT</MinValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <ParaSource>Man</ParaSource>
-        <ParaValue>0,1,2,7,8,9,10</ParaValue>
-        <EnModification>True</EnModification> 
-        <EnMultipleChoice>False</EnMultipleChoice> 
-        <Control>UploadInput</Control> 
-        <InputType>Aux</InputType>
-        <InputNum>0</InputNum>
-        <DateFrom>Aux</DateFrom>
-        <EnModification>True</EnModification>
-        <EnMultipleChoice>True</EnMultipleChoice>
-        <Control>UploadInput</Control>
-        <InputType>Aux</InputType>
-        <InputNum>0</InputNum>
-        <DateFrom>Aux</DateFrom>
-      </Parameter>
-    </Inputs>
-    <Outputs ParameterNum="1">
-      <Parameter>
-        <ParaName>VegetationPhenologyProduct</ParaName>
-        <ParaChsName>植被物候产品</ParaChsName>
-        <Description>植被物候产品</Description>
-        <ParaType>File</ParaType>
-        <DataType>tar.gz</DataType>
-        <ParaSource>Man</ParaSource>
-        <ParaValue>
-          D:\micro\WorkSpace\VegetationPhenology\Output\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho-VP.tar.gz</ParaValue>
-        <MaxValue>DEFAULT</MaxValue>
-        <MinValue>DEFAULT</MinValue>
-        <OptionValue>DEFAULT</OptionValue>
-        <NoDataValue>DEFAULT</NoDataValue>
-        <filePath>DEFAULT</filePath>
-      </Parameter>
-    </Outputs>
-  </AlgCompt>
-</Root>

vegetationPhenology/testxmlreading.py

@@ -51,12 +51,12 @@ def createcsv_roi_polygon(coordinates):
     return  polygon_str
 
 if __name__ == '__main__':
-    xmlpath = r"F:\MicroWorkspace\20240814tw\shp\20240819\landCover.xml"
+    xmlpath = r"G:\地表覆盖特征图-训练-吴学箫\2024010_地物分类_feature_POL\hecheng\ROI\rengonggouxuan_3.xml"
 
     tree_obj = ET.parse(xmlpath)
-    # csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
-    csv_header = ['parent_id', 'id', 'covernm', 'roi_polygon']
-    csvpath = r"F:\MicroWorkspace\20240814tw\shp\20240819\LandCover.csv"
+    csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
+    # csv_header = ['parent_id', 'id', 'covernm', 'roi_polygon']
+    csvpath = r"G:\地表覆盖特征图-训练-吴学箫\2024010_地物分类_feature_POL\hecheng\ROI\rengonggouxuan_3.csv"
     # csvcreateTitile(csvpath,csv_header)
     csvfile(csvpath,csv_header)
     # 得到所有匹配Region 标签的Element对象的list集合