地表覆盖类型增加四个计划信息到特征中，修改粗糙度产品掩膜

2024-01-10 16:24:57 +08:00 · 2024-01-10 16:24:57 +08:00 · 32267348fe
parent 8b1a71ef36
commit 32267348fe
12 changed files with 80 additions and 61 deletions
--- a/.gitignore
+++ b/.gitignore
@ -30,13 +30,8 @@
 /vegetationPhenology/tool/
 /vegetationPhenology/VegetationPhenology/
 /backScattering/baseTool/
-/landcover_c_sar/dist/
-/leafAreaIndex/dist/
-
-/soilSalinity/tool/
-/landcover_c_sar/tool/
 /atmosphericDelay-C-SAR/tool/
 /deformation-C-SAR/tool/
 /dem-C-SAR/tool/
 /soilMoistureTop/tool/
-/backScattering/dist/
+
--- a/Ortho/Ortho.xml
+++ b/Ortho/Ortho.xml
@ -45,7 +45,7 @@
        <ParaType>File</ParaType>
        <DataType>tar.gz</DataType>
        <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\202306hb\sar_img\GF3B_SYC_QPSI_008316_E116.1_N43.3_20230622_L1A_AHV_L10000202892.tar.gz</ParaValue>
+        <ParaValue>E:\VegetationPhenology-likun\lijiang\GF3B_KSC_QPSI_007906_E100.2_N27.0_20230525_L1A_AHV_L10000190531.tar.gz</ParaValue>
        <EnModification>True</EnModification> 
        <EnMultipleChoice>False</EnMultipleChoice> 
        <Control>File</Control> 
@ -60,7 +60,7 @@
        <ParaType>File</ParaType>
        <DataType>File</DataType>
        <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\202306hb\dem</ParaValue>
+        <ParaValue>E:\VegetationPhenology-likun\lijiang\dem</ParaValue>
        <EnModification>True</EnModification> 
        <EnMultipleChoice>True</EnMultipleChoice> 
        <Control>File</Control> 
@ -92,7 +92,7 @@
        <ParaType>File</ParaType>
        <DataType>tar.gz</DataType>
 		<ParaSource>Cal</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\Ortho\Output\GF3B_SYC_QPSI_008316_E116.1_N43.3_20230622_L1A_AHV_L10000202892-ortho.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\Ortho\Output\GF3B_KSC_QPSI_007906_E100.2_N27.0_20230525_L1A_AHV_L10000190531-ortho.tar.gz</ParaValue>
        <MaxValue>DEFAULT</MaxValue>
        <MinValue>DEFAULT</MinValue>
        <OptionValue>DEFAULT</OptionValue>
--- a/atmosphericDelay-C-SAR/Delay_test.py
+++ b/atmosphericDelay-C-SAR/Delay_test.py
@ -4,9 +4,10 @@ from tool.algorithm.image import ImageHandle
 from tool.algorithm.algtools.PreProcess import PreProcess as pp
 import gc
 import os
+import sys
 from pathlib import Path

-env_str = os.path.split(os.path.realpath(__file__))[0]
+env_str = os.path.dirname(os.path.abspath(sys.argv[0]))
 os.environ['PROJ_LIB'] = env_str


@ -60,11 +61,11 @@ def resampe_image(aux_dry_wet_unresame_path, mas_dry_wet_unresame_path, temp_dir


 if __name__ == '__main__':
-    mas_nc = r'F:\干涉大气延迟校正\大气延迟检验\大气延迟检验\ERA5_N25_N35_E115_E125_20220910_10.nc'
-    aux_nc = r'F:\干涉大气延迟校正\大气延迟检验\大气延迟检验\ERA5_N25_N35_E115_E125_20220922_10.nc'
-    dem_file = r''
-    temp_dir = 'F://干涉大气延迟校正//大气延迟检验//大气延迟检验//nc_file//test//'
-    out_path = 'F://干涉大气延迟校正//大气延迟检验//大气延迟检验//nc_file//'
+    mas_nc = r'D:\micro\WorkSpace\AtmosphericDelay\ERA5\ERA5_N42.0_N46.0_E114.0_E118.0_20230615_08.nc'
+    aux_nc = r'D:\micro\WorkSpace\AtmosphericDelay\ERA5\ERA5_N42.0_N46.0_E114.0_E118.0_20230910_08.nc'
+    dem_file = r'D:\micro\WorkSpace\AtmosphericDelay\ERA5\cut_dem.tif'
+    temp_dir = r'D:\micro\WorkSpace\AtmosphericDelay\test_ztd\test'
+    out_path = r'D:\micro\WorkSpace\AtmosphericDelay\test_ztd' + '\\'

    # 读取气象数据
    ncHandle = NcHandle()
@ -76,23 +77,23 @@ if __name__ == '__main__':

    base_file = out_path + "MasterNC_geo_h.tif"  # 用来获取裁剪后气象数据的经纬度、分辨率等信息
    #
-    # # 1.2 计算主、辅影像垂直分层湿干延迟值，并保存干湿延迟图
-    # m_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, m_temp, m_re_hum, m_geo, dem_file)  # 主影像垂直分层干湿延迟值数组
-    # m_dry_wet_path = out_path + "m_dry_wet.tif"  # 主影像干湿延迟图保存路径
-    # Ady().write_dry_wet_tif(dem_file, base_file, m_cdstack_dry_wet, m_dry_wet_path)
-    #
-    # a_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, a_temp, a_re_hum, a_geo, dem_file)  # 辅影像垂直分层干湿延迟值数组
-    # a_dry_wet_path = out_path + "a_dry_wet.tif"  # 辅影像干湿延迟图保存路径
-    # Ady().write_dry_wet_tif(dem_file, base_file, a_cdstack_dry_wet, a_dry_wet_path)
-    # #
+    # 1.2 计算主、辅影像垂直分层湿干延迟值，并保存干湿延迟图
+    m_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, m_temp, m_re_hum, m_geo, dem_file)  # 主影像垂直分层干湿延迟值数组
+    m_dry_wet_path = out_path + "m_dry_wet.tif"  # 主影像干湿延迟图保存路径
+    Ady().write_dry_wet_tif(dem_file, base_file, m_cdstack_dry_wet, m_dry_wet_path)
+
+    a_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, a_temp, a_re_hum, a_geo, dem_file)  # 辅影像垂直分层干湿延迟值数组
+    a_dry_wet_path = out_path + "a_dry_wet.tif"  # 辅影像干湿延迟图保存路径
+    Ady().write_dry_wet_tif(dem_file, base_file, a_cdstack_dry_wet, a_dry_wet_path)
    #
    # aux_dry_wet_resamed_path, mas_dry_wet_resamed_path = resampe_image(a_dry_wet_path, m_dry_wet_path, temp_dir)  # 参照气象数据分辨率进行重采样
-    #
-    # gc.collect()  # 回收内存
-    # # 1.4 输出主辅影像ztd数组
-    # a_ztd = Ady().calc_ztd(a_dry_wet_path, dem_file)  # 辅影像的ztd数组
-    # m_ztd = Ady().calc_ztd(m_dry_wet_path, dem_file)  # 主影像的ztd数组
-    # out_aux_ztd_path = out_path + "aux_ztd.tif"
-    # out_mas_ztd_path = out_path + "mas_ztd.tif"
-    # Ady().write_ztd_tif(dem_file, base_file, a_ztd, out_aux_ztd_path)  # ztd数组->ztd影像
-    # Ady().write_ztd_tif(dem_file, base_file, m_ztd, out_mas_ztd_path)  # ztd数组->ztd影像
+
+    gc.collect()  # 回收内存
+    # 1.4 输出主辅影像ztd数组
+    a_ztd = Ady().calc_ztd(a_dry_wet_path, dem_file)  # 辅影像的ztd数组
+    m_ztd = Ady().calc_ztd(m_dry_wet_path, dem_file)  # 主影像的ztd数组
+    out_aux_ztd_path = out_path + "aux_ztd.tif"
+    out_mas_ztd_path = out_path + "mas_ztd.tif"
+    Ady().write_ztd_tif(dem_file, base_file, a_ztd, out_aux_ztd_path)  # ztd数组->ztd影像
+    Ady().write_ztd_tif(dem_file, base_file, m_ztd, out_mas_ztd_path)  # ztd数组->ztd影像
+
--- a/landcover_c_sar/LandCover.xml
+++ b/landcover_c_sar/LandCover.xml
@ -87,7 +87,7 @@
        <ParaType>Value</ParaType>
        <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>0,1,2,7,8,9,10</ParaValue>
+		<ParaValue>0,1,2,7,8,9,10,11,12,13</ParaValue>
        <EnModification>True</EnModification> 
        <EnMultipleChoice>True</EnMultipleChoice> 
        <Control>UploadInput</Control> 
@ -135,7 +135,7 @@
        <ParaType>File</ParaType>
        <DataType>tar.gz</DataType>
 		<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\GF3B_KSC_QPSI_010328_E86.1_N44.5_20231109_L1A_AHV_L10000262134-ortho-LANDCLASS.tar.gz</ParaValue>
      </Parameter>
    </Outputs>
  </AlgCompt>
--- a/landcover_c_sar/LandCoverAuxData.py
+++ b/landcover_c_sar/LandCoverAuxData.py
@ -122,10 +122,15 @@ class LandCoverMeasCsv:
                        # 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
+            # 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)
--- a/landcover_c_sar/LandCoverMain.py
+++ b/landcover_c_sar/LandCoverMain.py
@ -116,7 +116,7 @@ class LandCoverMain:
        checkFlag, self.__parameters_dic = self.__check_handler.check_input_paras(self.__input_paras)
        self.__workspace_path = self.__alg_xml_handler.get_workspace_path()
        self.__create_work_space()
-        self.__processing_paras = InitPara.init_processing_paras(self.__input_paras)
+        self.__processing_paras = InitPara.init_processing_paras(self.__input_paras, self.__workspace_preprocessed_path)
        self.__processing_paras.update(self.get_tar_gz_inf(self.__processing_paras["sar_path0"]))
        SrcImageName = os.path.split(self.__input_paras["AHV"]['ParaValue'])[1].split('.tar.gz')[0]
        result_name = SrcImageName + tar + ".tar.gz"
@ -498,6 +498,21 @@ class LandCoverMain:
            out_path = os.path.join(dir, name)
            self.calInterpolation_bil_Wgs84_rc_sar_sigma(self.__processing_paras['paraMeter'],
                                                     self.__preprocessed_paras['sim_ori'], file, out_path)
+
+        calOutDir = os.path.join(self.__workspace_processing_path, 'cal\\')  # 添加四极化后向散射系数到特征图中
+        if not os.path.exists(calOutDir):
+            os.makedirs(calOutDir)
+        para_names_l1a = ["HH", "VV", "HV", "VH"]
+        for key in para_names_l1a:
+            name = key + '_geo.tif'
+            out_path = os.path.join(dir, name)
+            calOut_path = os.path.join(calOutDir, key + '_cal.tif')
+            AHVToPolsarpro.sar_backscattering_sigma(self.__preprocessed_paras[key],
+                                                    self.__processing_paras['Origin_META'], calOut_path)
+            self.calInterpolation_bil_Wgs84_rc_sar_sigma(self.__processing_paras['paraMeter'],
+                                                         self.__preprocessed_paras['sim_ori'],
+                                                         calOut_path, out_path)
+
        return dir

    def process_handle(self, start):
@ -549,7 +564,7 @@ class LandCoverMain:
        logger.info('progress bar: 50%')

        # 生成最优特征子集训练集
-        X_train, Y_train, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_geo, 0.07, 0.85)
+        X_train, Y_train, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_geo, 0.07, 0.85)  #  0.07, 0.85

        # 训练模型
        cost = self.__processing_paras["Cost"]
--- a/soilSalinity/SoilSalinity.xml
+++ b/soilSalinity/SoilSalinity.xml
@ -138,7 +138,7 @@
        <ParaType>File</ParaType>
        <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\SoilSalinity\Output\GF3B_SYC_QPSI_008316_E116.2_N43.7_20230622_L1A_AHV_L10000202891-ortho-salinty.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SoilSalinity\Output\GF3B_SYC_QPSI_008316_E116.2_N43.7_20230622_L1A_AHV_L10000202891-ortho-SSAA.tar.gz</ParaValue>
        <MaxValue>DEFAULT</MaxValue>
        <MinValue>DEFAULT</MinValue>
        <OptionValue>DEFAULT</OptionValue>
--- a/surfaceRoughness_oh2004/SurfaceRoughness.xml
+++ b/surfaceRoughness_oh2004/SurfaceRoughness.xml
@ -38,7 +38,7 @@
        <ParaType>File</ParaType>
        <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>E:\202306pj\GF3B_MYC_QPSI_008114_E121.6_N40.9_20230608_L1A_AHV_L10000196489-ortho.tar.gz</ParaValue>
+		<ParaValue>D:\micro\WorkSpace\Ortho\Output\GF3B_SYC_QPSI_008316_E116.1_N43.3_20230622_L1A_AHV_L10000202892-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>E:\202306pj\n51_40_2020lc030.tif</ParaValue>
+		<ParaValue>D:\Dict\50T_20220101-20230101.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;50;70;71;72;83;74;90</ParaValue>
+		<ParaValue>empty</ParaValue>
        <EnModification>True</EnModification> 
        <EnMultipleChoice>False</EnMultipleChoice> 
        <Control>UploadInput</Control> 
@ -98,7 +98,7 @@
        <ParaType>File</ParaType>
        <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>E:\202306pj\L9NDVI_GF3B_175394.tif</ParaValue>
+		<ParaValue>F:\202306hb\NDVI\S2_202306_NDVI.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\GF3B_MYC_QPSI_008114_E121.6_N40.9_20230608_L1A_AHV_L10000196489-ortho-SR.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SurfaceRoughness\Output\GF3B_SYC_QPSI_008316_E116.1_N43.3_20230622_L1A_AHV_L10000202892-ortho-SR.tar.gz</ParaValue>
      </Parameter>
    </Outputs>
  </AlgCompt>
--- a/surfaceRoughness_oh2004/SurfaceRoughnessMain.py
+++ b/surfaceRoughness_oh2004/SurfaceRoughnessMain.py
@ -328,7 +328,7 @@ class MoistureMain:
        product_path = os.path.join(self.__product_dic, SrcImageName)
        # 获取影像roi区域

-        roi.cal_roi(product_path, product_geo_path, bare_land_mask_path, background_value=0)
+        roi.cal_roi(product_path, product_geo_path, bare_land_mask_path, background_value=-9999)

        # 生成快视图
        self.imageHandler.write_quick_view(product_path)
--- a/tool/algorithm/algtools/ROIAlg.py
+++ b/tool/algorithm/algtools/ROIAlg.py
@ -228,11 +228,11 @@ class ROIAlg:
        logger.info("cal_roi success, path: %s", out_tif_path)
        return True

-# if __name__ == '__main__':
-#     dir = r'G:\MicroWorkspace\C-SAR\SoilMoisture\Temporary\processing/'
-#     out_tif_path = dir + 'soil_moisture_roi.tif'
-#     in_tif_path = dir + 'soil_moisture.tif'
-#     mask_path = dir + 'bare_land_mask.tif'
-#     background_value = np.nan
-#     ROIAlg.cal_roi(out_tif_path, in_tif_path, mask_path, background_value)
-#     pass
+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)
+    pass
--- a/vegetationPhenology/VegetationPhenology.xml
+++ b/vegetationPhenology/VegetationPhenology.xml
@ -37,7 +37,7 @@
        <ParaType>File</ParaType>
        <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>E:\VegetationPhenology-likun\rusuoces\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764-ortho.tar.gz</ParaValue>
+		<ParaValue>E:\VegetationPhenology-likun\lijiang\GF3B_KSC_QPSI_007906_E100.2_N27.0_20230525_L1A_AHV_L10000190531-ortho.tar.gz</ParaValue>
        <MaxValue>DEFAULT</MaxValue>
        <MinValue>DEFAULT</MinValue>
        <OptionValue>DEFAULT</OptionValue>
@ -51,7 +51,7 @@
        <ParaType>File</ParaType>
        <DataType>csv</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>E:\VegetationPhenology-likun\rusuoces\VegetationPhe_samples.csv</ParaValue>
+        <ParaValue>E:\VegetationPhenology-likun\lijiang\VegetationPhe_samples.csv</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
        <MinValue>DEFAULT</MinValue>
        <OptionValue>DEFAULT</OptionValue>
@ -65,7 +65,7 @@
        <ParaType>File</ParaType>
        <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>E:\VegetationPhenology-likun\rusuoces\N47_25_2020LC030\n47_25_2020lc030.tif</ParaValue>
+		<ParaValue>E:\VegetationPhenology-likun\lijiang\landcover.tif</ParaValue>
        <EnModification>True</EnModification>
        <EnMultipleChoice>False</EnMultipleChoice>
        <Control>File</Control>
@ -80,7 +80,7 @@
        <ParaType>Value</ParaType>
        <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>10</ParaValue>
+		<ParaValue>3</ParaValue>
        <EnModification>True</EnModification>
        <EnMultipleChoice>False</EnMultipleChoice>
        <Control>UploadInput</Control>
@ -130,7 +130,7 @@
        <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>
+        <ParaValue>D:\micro\WorkSpace\VegetationPhenology\Output\GF3B_KSC_QPSI_007906_E100.2_N27.0_20230525_L1A_AHV_L10000190531-ortho-VP.tar.gz</ParaValue>
        <MaxValue>DEFAULT</MaxValue>
        <MinValue>DEFAULT</MinValue>
        <OptionValue>DEFAULT</OptionValue>
--- a/vegetationPhenology/VegetationPhenologyAuxData.py
+++ b/vegetationPhenology/VegetationPhenologyAuxData.py
@ -320,9 +320,13 @@ class PhenoloyMeasCsv_geo:
                    if train_data[3] == [] :
                        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:
-            num_list.append(len(train_data[3]))
+            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])))
@ -331,8 +335,7 @@ class PhenoloyMeasCsv_geo:
            if(len(train_data[3]) > max_num):
                train_data[3] = random.sample(train_data[3], max_num)

-        if len(train_data_list) <= 1:
-            raise Exception('there is only one label type!', train_data_list)
+
        return train_data_list

    @staticmethod