diff --git a/leafAreaIndex/product.xml b/leafAreaIndex/product.xml
new file mode 100644
index 00000000..017cb135
--- /dev/null
+++ b/leafAreaIndex/product.xml
@@ -0,0 +1,60 @@
+
+
+ 后向散射系数
+ BackScattering
+ 5
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1
+ None
+ 参考产品介绍PDF
+
+
+
+
+ 德清
+
+
+
+
\ No newline at end of file
diff --git a/leafAreaIndex/sample_process.py b/leafAreaIndex/sample_process.py
new file mode 100644
index 00000000..c32132e5
--- /dev/null
+++ b/leafAreaIndex/sample_process.py
@@ -0,0 +1,202 @@
+#
+# 样本处理的相关的库
+#
+
+from tool.algorithm.image.ImageHandle import ImageHandler
+import math
+import numpy as np
+import random
+import scipy
+# 最小二乘求解非线性方程组
+from scipy.optimize import leastsq,fsolve,root
+from osgeo import gdal,gdalconst
+import pandas as pds
+from scipy import interpolate
+from multiprocessing import pool
+# 常量声明区域
+imageHandler=ImageHandler()
+
+
+# python 的函数类
+def read_sample_csv(csv_path):
+ """ 读取样本的csv
+ Args:
+ csv_path (string): 样本csv的地址，绝对路径
+ return:
+ [
+ ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数"],
+ ['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数"],......
+ ]
+ """
+ lai_csv=pds.read_csv(csv_path)# 代码测试区域
+ lai_csv=lai_csv.loc[:,['id','lon','lat','leaf',"cal"]]
+ result=[]
+ for i in range(len(lai_csv)):
+ result.append([
+ 0,
+ lai_csv.loc[i,'id'],
+ lai_csv.loc[i,'lon'], # lon,x
+ lai_csv.loc[i,'lat'], # lat,y
+ lai_csv.loc[i,'leaf'],
+ 10**(float(lai_csv.loc[i,'cal'])/10),
+ ])
+ return result
+def read_tiff(tiff_path):
+ """ 从文件中读取影像
+
+ Args:
+ tiff_path (string): 文件影像路径
+ """
+ im_proj, im_geotrans, im_arr=imageHandler.read_img(tiff_path)
+ return {
+ 'proj':im_proj,
+ 'geotrans':im_geotrans,
+ 'data':im_arr
+ }
+
+def ReprojectImages2(in_tiff_path,ref_tiff_path,out_tiff_path,resampleAlg=gdalconst.GRA_Bilinear):
+ """ 将输入影像重采样到参考影像的范围内
+
+ Args:
+ in_tiff_path (string): 输入影像
+ ref_tiff_path (string): 参考影像
+ out_tiff_path (string): 输出地址
+ resampleAlg (gadlconst): 插值方法
+ """
+ # 若采用gdal.Warp()方法进行重采样
+ # 获取输出影像信息
+ inputrasfile = gdal.Open(in_tiff_path, gdal.GA_ReadOnly)
+ inputProj = inputrasfile.GetProjection()
+ # 获取参考影像信息
+ referencefile = gdal.Open(ref_tiff_path, gdal.GA_ReadOnly)
+ referencefileProj = referencefile.GetProjection()
+ referencefileTrans = referencefile.GetGeoTransform()
+ bandreferencefile = referencefile.GetRasterBand(1)
+ x = referencefile.RasterXSize
+ y = referencefile.RasterYSize
+ nbands = referencefile.RasterCount
+ # 创建重采样输出文件（设置投影及六参数）
+ driver = gdal.GetDriverByName('GTiff')
+ output = driver.Create(out_tiff_path, x, y, nbands, bandreferencefile.DataType)
+ output.SetGeoTransform(referencefileTrans)
+ output.SetProjection(referencefileProj)
+ options = gdal.WarpOptions(srcSRS=inputProj, dstSRS=referencefileProj, resampleAlg=gdalconst.GRA_Bilinear)
+ gdal.Warp(output, in_tiff_path, options=options)
+
+def combine_sample_attr(sample_list,attr_tiff):
+ """ 构建样本
+
+ Args:
+ sample_list (list): 原样本
+ attr_tiff (string): 添加的属性数据
+
+ Returns:
+ list:[sample,new_attr]
+ """
+ result=[]
+ # 因为soil_tiff 的影像的 影像分辨率较低
+ inv_gt=gdal.InvGeoTransform(attr_tiff['geotrans'])
+ for sample_item in sample_list:
+ sample_lon=sample_item[2]
+ sample_lat=sample_item[3]
+ sample_in_tiff_x=inv_gt[0]+inv_gt[1]*sample_lon+inv_gt[2]*sample_lat # x
+ sample_in_tiff_y=inv_gt[3]+inv_gt[4]*sample_lon+inv_gt[5]*sample_lat # y
+ x_min=int(np.floor(sample_in_tiff_x))
+ x_max=int(np.ceil(sample_in_tiff_x))
+ y_min=int(np.floor(sample_in_tiff_y))
+ y_max=int(np.ceil(sample_in_tiff_y))
+ if x_min<0 or y_min<0 or x_max>=attr_tiff['data'].shape[1] or y_max>=attr_tiff['data'].shape[0]:
+ continue
+ #
+ """
+ f = interpolate.interp2d([0,0,1,1], [0,1,1,0],
+ [attr_tiff['data'][y_min,x_min],
+ attr_tiff['data'][y_max,x_min],
+ attr_tiff['data'][y_max,x_max],
+ attr_tiff['data'][y_min,x_min]
+ ], kind='linear')
+ interp_value=f(sample_in_tiff_x-x_min,sample_in_tiff_y-y_min)
+ sample_item.append(interp_value[0])
+ """
+ # 9x9
+ x_min=x_min-4 if x_min-9>=0 else 0
+ y_min=y_min-4 if y_min-9>=0 else 0
+ x_max=x_max+4 if x_max+490:
+ continue
+ if sample_soil<=0 or sample_soil>=1:
+ continue
+ if sample_lai<=0 or sample_lai>=20:
+ continue
+ result.append(item)
+ # 绘制分布图
+ # lai=[]
+ # sigma=[]
+ # csv_sigmas=[]
+ # text_label=[]
+ # for item in result:
+ # if len(item)==10:
+ # sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma,sample_NDVI=item
+ # else:
+ # sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma=item
+ # text_label.append(sample_code)
+ # lai.append(sample_lai)
+ # sigma.append(sample_sigma)
+ # csv_sigmas.append(csv_sigma)
+ # from matplotlib import pyplot as plt
+ # plt.scatter(np.array(lai),np.array(sigma),label="lai-tiff_sigma")
+ # for i in range(len(sigma)):
+ # plt.annotate(text_label[i], xy = (lai[i], sigma[i])) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
+ #
+ # plt.scatter(np.array(lai),np.array(csv_sigmas),label="lai-csv_sigmas")
+ # for i in range(len(csv_sigmas)):
+ # plt.annotate(text_label[i], xy = (lai[i],csv_sigmas[i])) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
+ # plt.legend()
+ # plt.show()
+
+ return result
+
+
+
+def split_sample_list(sample_list,train_ratio):
+ """ 切分样本比值
+
+ Args:
+ sample_list (list): 样本列表
+ train_ratio (double): 训练样本的比重
+
+ Returns:
+ list: [sample_train,sample_test]
+ """
+ sample_train=[]
+ sample_test=[]
+ n=len(sample_list)
+ for i in range(n):
+ if random.random()<=train_ratio:
+ sample_train.append(sample_list[i])
+ else:
+ sample_test.append(sample_list[i])
+ return [sample_train,sample_test]
\ No newline at end of file
diff --git a/soilMoistureTop/SoilMoistureMain.py b/soilMoistureTop/SoilMoistureMain.py
index 7bb6512c..871e112b 100644
--- a/soilMoistureTop/SoilMoistureMain.py
+++ b/soilMoistureTop/SoilMoistureMain.py
@@ -249,7 +249,7 @@ class MoistureMain:
bp = BlockProcess()
# block_size = bp.get_block_size(self.__rows, self.__cols,block_size_config)
block_size = bp.get_block_size(self.__rows, self.__cols)
- bp.cut(self.__workspace_preprocessed_path, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
+ bp.cut_new(self.__workspace_preprocessed_path, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
logger.info('blocking tifs success!')
img_dir, img_name = bp.get_file_names(self.__workspace_block_tif_path, ['tif'])
@@ -339,7 +339,7 @@ class MoistureMain:
h = ImageHandler.get_img_height(self.__preprocessed_paras['HH'])
out_path = self.__workspace_processing_path[0:-1]
tif_type = bp.get_tif_dtype(self.__preprocessed_paras['HH'])
- bp.combine(data_dir, w, h, out_path, file_type=['tif'], datetype=tif_type)
+ bp.combine_new(data_dir, w, h, out_path, file_type=['tif'], datetype=tif_type)
# 添加地理信息
soil_moisture_path = self.__workspace_processing_path + 'soil_moisture.tif'
diff --git a/soilMoistureTop/product.xml b/soilMoistureTop/product.xml
new file mode 100644
index 00000000..017cb135
--- /dev/null
+++ b/soilMoistureTop/product.xml
@@ -0,0 +1,60 @@
+
+
+ 后向散射系数
+ BackScattering
+ 5
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1
+ None
+ 参考产品介绍PDF
+
+
+
+
+ 德清
+
+
+
+
\ No newline at end of file
diff --git a/soilSalinity/lee_refined_filter_T3.exe b/soilSalinity/lee_refined_filter_T3.exe
new file mode 100644
index 00000000..3b4839c1
Binary files /dev/null and b/soilSalinity/lee_refined_filter_T3.exe differ
diff --git a/soilSalinity/product.xml b/soilSalinity/product.xml
new file mode 100644
index 00000000..017cb135
--- /dev/null
+++ b/soilSalinity/product.xml
@@ -0,0 +1,60 @@
+
+
+ 后向散射系数
+ BackScattering
+ 5
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1
+ None
+ 参考产品介绍PDF
+
+
+
+
+ 德清
+
+
+
+
\ No newline at end of file
diff --git a/surfaceRoughness_oh2004/product.xml b/surfaceRoughness_oh2004/product.xml
new file mode 100644
index 00000000..017cb135
--- /dev/null
+++ b/surfaceRoughness_oh2004/product.xml
@@ -0,0 +1,60 @@
+
+
+ 后向散射系数
+ BackScattering
+ 5
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1
+ None
+ 参考产品介绍PDF
+
+
+
+
+ 德清
+
+
+
+
\ No newline at end of file
diff --git a/vegetationPhenology/product.xml b/vegetationPhenology/product.xml
new file mode 100644
index 00000000..017cb135
--- /dev/null
+++ b/vegetationPhenology/product.xml
@@ -0,0 +1,60 @@
+
+
+ 后向散射系数
+ BackScattering
+ 5
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1
+ None
+ 参考产品介绍PDF
+
+
+
+
+ 德清
+
+
+
+
\ No newline at end of file
diff --git a/vegetationPhenology/testxmlreading.py b/vegetationPhenology/testxmlreading.py
new file mode 100644
index 00000000..cc3a0377
--- /dev/null
+++ b/vegetationPhenology/testxmlreading.py
@@ -0,0 +1,79 @@
+#encoding=utf-8
+import xml.etree.ElementTree as ET
+import pandas as pd
+import csv
+
+def xml2csv(xmlpath):
+ tree_obj = ET.parse(xmlpath)
+
+ # 得到所有匹配Region 标签的Element对象的list集合
+ list_Region = tree_obj.findall("Region")
+ for Region in list_Region:
+ # 几何面对应的类(phenology_name)在标签
+ Region_dict = Region.attrib
+ phenology_name = Region_dict.get("name")
+ print(phenology_name)
+ list_GeometryDef = Region.findall("GeometryDef")
+ list_Polygon = list_GeometryDef[0].findall("Polygon") # 获得该类下的几何面list
+ for polygon in list_Polygon:
+ # 从polygon list中获取得到标签的数据 注意是空格分隔的和csv中不同
+ Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
+ # POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
+ print("value")
+
+# 向csv中写
+def csvfile(csvpath,data):
+
+ with open(csvpath, 'a', newline='') as file:
+ # 2. step
+ writer = csv.writer(file)
+ # data example
+ #data = ["This", "is", "a", "Test"]
+ writer.writerow(data)
+
+
+# Define the structure of the data
+#data示例 student_header = ['name', 'age', 'major', 'minor']
+def csvcreateTitile(csvpath,data):
+ # 1. Open a new CSV file
+ with open(csvpath, 'w') as file:
+ # 2. Create a CSV writer
+ writer = csv.writer(file)
+ # 3. Write data to the file
+ writer.writerow(data)
+
+# 将列表中的坐标对转换为字符串
+def createcsv_roi_polygon(coordinates):
+ coord_str = ', '.join([f'{coordinates[i]} {coordinates[i + 1]}' for i in range(0, len(coordinates), 2)])
+ # 构建最终的POLYGON字符串
+ polygon_str = f'POLYGON(({coord_str}))'
+ print(polygon_str)
+ return polygon_str
+
+if __name__ == '__main__':
+ xmlpath = r"E:\MicroWorkspace\GF3A_nanjing\input-ortho\test_shp\test.xml"
+
+ tree_obj = ET.parse(xmlpath)
+ csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
+ csvpath = r"E:\MicroWorkspace\GF3A_nanjing\input-ortho\test_shp\test.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
+ list_Region = tree_obj.findall("Region")
+ for Region in list_Region:
+ # 几何面对应的类(phenology_name)在标签
+ Region_dict = Region.attrib
+ phenology_name = Region_dict.get("name")
+ print(phenology_name)
+ # list_GeometryDef = Region.findall("GeometryDef")
+ list_Polygon = Region.findall(".//Polygon") # 获得该类下的几何面list
+ count = 1
+ for polygon in list_Polygon:
+ # 从polygon list中获取得到标签的数据 注意是空格分隔的和csv中不同
+ Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
+ # 将坐标和ploygon对应的写入到.csv中
+ polygon_str=createcsv_roi_polygon(Coordinates_list)
+ # POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
+ data = ['0', count, phenology_name, polygon_str]
+ csvfile(csvpath,data)
+ count += 1