Python遥感开发之分段读取和保存遥感数据

Python遥感开发之分段读取和保存遥感数据

  • 1 分段读取数据
  • 2 实现分批读取数据以及进行计算
  • 3 实现分批保存成TIF文件(所有完整代码)
  • 4 分段TIF整合到一个TIF
  • 5 生成一个空白TIF(每个像元值为0的TIF)

前言:当遇到批量读取大量遥感数据进行运算的时候,如果不进行分段读取操作的话,电脑内存可能面临着不够使用的情况,所以我们要进行分段读取数据然后进行运算,运算结束之后把这段数据保存成tif文件。


1 分段读取数据


如图所示,有三个这样的数据,且该数据为5600行6800列,我们可以分成10个批次分段读取该TIF数据,10个批次以此为0,560,1120,1680,2240,2800,3360,3920,4480,5040,5600。
代码实现:

import os
import numpy as np
from osgeo import gdal, gdalnumeric

def read_tif(filepath):
    dataset = gdal.Open(filepath)
    col = dataset.RasterXSize#图像长度
    row = dataset.RasterYSize#图像宽度
    geotrans = dataset.GetGeoTransform()#读取仿射变换
    proj = dataset.GetProjection()#读取投影
    data = dataset.ReadAsArray()#转为numpy格式
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return [col, row, geotrans, proj, data]

def read_tif02(file):
    data = gdalnumeric.LoadFile(file)
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return data

def get_all_file_name(ndvi_file):
    list1=[]
    if os.path.isdir(ndvi_file):
        fileList = os.listdir(ndvi_file)
        for f in fileList:
            file_name= ndvi_file+"\\"+f
            list1.append(file_name)
        return list1
    else:
        return []

if __name__ == '__main__':
    file_ndvi = r"D:\AAWORK\work\研究方向\研究方向01作物分类内容\风云数据\MERSI-II植被指数旬产品(250M)\NDVI主合成"
    file_out = r"D:\AAWORK\work\2021NDVISUM.tif"
    ndvi_file_list = get_all_file_name(file_ndvi)
    col00, row00, geotrans00, proj00, data_00_ndvi = read_tif(ndvi_file_list[0])
    data_01_ndvi = read_tif02(ndvi_file_list[1])
    data_02_ndvi = read_tif02(ndvi_file_list[2])
    list_row = [0,560,1120,1680,2240,2800,3360,3920,4480,5040,5600]
    for index,i in enumerate(list_row):
        if index <= len(list_row)-2:
            print(list_row[index],list_row[index+1])
            #分段进行操作
            # sum_list = get_section(list_row[index],list_row[index+1],col00,data_00_ndvi,data_01_ndvi,data_02_ndvi)
            # 分段进行保存
            # save_section(sum_list, list_row[index], list_row[index+1], col00,data_00_ndvi,data_01_ndvi,data_02_ndvi)

Python遥感开发之分段读取和保存遥感数据_第1张图片

2 实现分批读取数据以及进行计算

拿到开始的行和结束的行数,进行分批读取数据并进行计算,(这里求和求的是整数,如有需要的话可以自己更改)代码如下:

import os
import tensorflow as tf
import numpy as np
import pandas as pd
from osgeo import gdal, gdalnumeric

def get_sum_list(data_list):
    list1 = []
    for data in data_list:
        sum = 0
        for d in data:
            if not np.isnan(d):
                sum = sum+d
        list1.append(int(sum))
    return list1

def read_tif(filepath):
    dataset = gdal.Open(filepath)
    col = dataset.RasterXSize#图像长度
    row = dataset.RasterYSize#图像宽度
    geotrans = dataset.GetGeoTransform()#读取仿射变换
    proj = dataset.GetProjection()#读取投影
    data = dataset.ReadAsArray()#转为numpy格式
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return [col, row, geotrans, proj, data]

def read_tif02(file):
    data = gdalnumeric.LoadFile(file)
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return data

def get_all_file_name(ndvi_file):
    list1=[]
    if os.path.isdir(ndvi_file):
        fileList = os.listdir(ndvi_file)
        for f in fileList:
            file_name= ndvi_file+"\\"+f
            list1.append(file_name)
        return list1
    else:
        return []

def get_nan_sum(ndvi_list):
    """
    得到NAN数据的个数
    :param ndvi_list:
    :return:
    """
    count = 0
    for ndvi in ndvi_list:
        if np.isnan(ndvi):
            count = count+1
    return count

def get_section(row0, row1, col1,data1,data2,data3):
    """
    分段读取数据,读取的数据进行计算
    :param row0:
    :param row1:
    :param col1:
    :param data1:
    :param data2:
    :param data3:
    :return:
    """
    list1 = []
    for i in range(row0, row1):  # 行
        for j in range(0, col1):  # 列
            ndvi_list = []
            ndvi_list.append(data1[i][j])
            ndvi_list.append(data2[i][j])
            ndvi_list.append(data3[i][j])
            if get_nan_sum(ndvi_list)>1:
                pass
            else:
                list1.append(ndvi_list)
            ndvi_list = None
    sum_list = get_sum_list(list1)
    list1 = None
    return sum_list


if __name__ == '__main__':
    file_ndvi = r"D:\AAWORK\work\研究方向\研究方向01作物分类内容\风云数据\MERSI-II植被指数旬产品(250M)\NDVI主合成"
    file_out = r"D:\AAWORK\work\2021NDVISUM.tif"
    ndvi_file_list = get_all_file_name(file_ndvi)
    col00, row00, geotrans00, proj00, data_00_ndvi = read_tif(ndvi_file_list[0])
    data_01_ndvi = read_tif02(ndvi_file_list[1])
    data_02_ndvi = read_tif02(ndvi_file_list[2])
    list_row = [0,560,1120,1680,2240,2800,3360,3920,4480,5040,5600]
    for index,i in enumerate(list_row):
        if index <= len(list_row)-2:
            print(list_row[index],list_row[index+1])
            #分段进行操作
            sum_list = get_section(list_row[index],list_row[index+1],col00,data_00_ndvi,data_01_ndvi,data_02_ndvi)
            print(np.array(sum_list))
            # 分段进行保存
            # save_section(sum_list, list_row[index], list_row[index+1], col00,data_00_ndvi,data_01_ndvi,data_02_ndvi)

Python遥感开发之分段读取和保存遥感数据_第2张图片

3 实现分批保存成TIF文件(所有完整代码)

在2中已经得到了每一批的list结果,我们拿到list结果之后,可以进行保存成tif文件。代码如下:

import os
import numpy as np
from osgeo import gdal, gdalnumeric

def get_sum_list(data_list):
    list1 = []
    for data in data_list:
        sum = 0
        for d in data:
            if not np.isnan(d):
                sum = sum+d
        list1.append(int(sum))
    return list1

def read_tif(filepath):
    dataset = gdal.Open(filepath)
    col = dataset.RasterXSize#图像长度
    row = dataset.RasterYSize#图像宽度
    geotrans = dataset.GetGeoTransform()#读取仿射变换
    proj = dataset.GetProjection()#读取投影
    data = dataset.ReadAsArray()#转为numpy格式
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return [col, row, geotrans, proj, data]

def read_tif02(file):
    data = gdalnumeric.LoadFile(file)
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return data

def get_all_file_name(ndvi_file):
    list1=[]
    if os.path.isdir(ndvi_file):
        fileList = os.listdir(ndvi_file)
        for f in fileList:
            file_name= ndvi_file+"\\"+f
            list1.append(file_name)
        return list1
    else:
        return []

def save_tif(data, file, output):
    """
    保存成tif
    :param data:
    :param file:
    :param output:
    :return:
    """
    ds = gdal.Open(file)
    shape = data.shape
    driver = gdal.GetDriverByName("GTiff")
    dataset = driver.Create(output, shape[1], shape[0], 1, gdal.GDT_Float32)#保存的数据类型
    dataset.SetGeoTransform(ds.GetGeoTransform())
    dataset.SetProjection(ds.GetProjection())
    dataset.GetRasterBand(1).WriteArray(data)

def get_nan_sum(ndvi_list):
    """
    得到NAN数据的个数
    :param ndvi_list:
    :return:
    """
    count = 0
    for ndvi in ndvi_list:
        if np.isnan(ndvi):
            count = count+1
    return count

def get_section(row0, row1, col1,data1,data2,data3):
    """
    分段读取数据,读取的数据进行计算
    :param row0:
    :param row1:
    :param col1:
    :param data1:
    :param data2:
    :param data3:
    :return:
    """
    list1 = []
    for i in range(row0, row1):  # 行
        for j in range(0, col1):  # 列
            ndvi_list = []
            ndvi_list.append(data1[i][j])
            ndvi_list.append(data2[i][j])
            ndvi_list.append(data3[i][j])
            if get_nan_sum(ndvi_list)>1:
                pass
            else:
                list1.append(ndvi_list)
            ndvi_list = None
    sum_list = get_sum_list(list1)
    list1 = None
    return sum_list

def save_section(sum_list, row0, row1, col1,data1,data2,data3):
    """
    保存分段的数据
    :param sum_list:
    :param row0:
    :param row1:
    :param col1:
    :param data1:
    :param data2:
    :param data3:
    :return:
    """
    file = r"D:\AAWORK\work\研究方向\研究方向01作物分类内容\风云数据\MERSI-II植被指数旬产品(250M)\kongbai_zhu_250m.tif"#这是一个空白数据,每个像元的值为0
    data = read_tif02(file)
    m = 0
    for i in range(row0, row1):  # 行
        for j in range(0, col1):  # 列
            ndvi_list = []
            ndvi_list.append(data1[i][j])
            ndvi_list.append(data2[i][j])
            ndvi_list.append(data3[i][j])
            if get_nan_sum(ndvi_list)>1:
                pass
            else:
                data[i][j] = sum_list[m]
                m = m + 1
    save_tif(data,file,file_out.replace(".tif","_"+str(row0)+"_"+str(row1)+".tif"))
    data = None


if __name__ == '__main__':
    file_ndvi = r"D:\AAWORK\work\研究方向\研究方向01作物分类内容\风云数据\MERSI-II植被指数旬产品(250M)\NDVI主合成"
    file_out = r"D:\AAWORK\work\2021NDVISUM.tif"
    ndvi_file_list = get_all_file_name(file_ndvi)
    col00, row00, geotrans00, proj00, data_00_ndvi = read_tif(ndvi_file_list[0])
    data_01_ndvi = read_tif02(ndvi_file_list[1])
    data_02_ndvi = read_tif02(ndvi_file_list[2])
    list_row = [0,560,1120,1680,2240,2800,3360,3920,4480,5040,5600]
    for index,i in enumerate(list_row):
        if index <= len(list_row)-2:
            print(list_row[index],list_row[index+1])
            #分段进行操作
            sum_list = get_section(list_row[index],list_row[index+1],col00,data_00_ndvi,data_01_ndvi,data_02_ndvi)
            print(np.array(sum_list))
            # 分段进行保存
            save_section(sum_list, list_row[index], list_row[index+1], col00,data_00_ndvi,data_01_ndvi,data_02_ndvi)


Python遥感开发之分段读取和保存遥感数据_第3张图片

4 分段TIF整合到一个TIF

我们要把上述10个TIF文件整合到一个TIF文件里,方法很多,我这里提供一个方法,供大家使用,代码如下:

import os

from osgeo import gdalnumeric, gdal
import numpy as np


def get_all_file_name(file):
    list1=[]
    if os.path.isdir(file):
        fileList = os.listdir(file)
        for f in fileList:
            file_name= file+"\\"+f
            list1.append(file_name)
        return list1
    else:
        return []

def read_tif(filepath):
    dataset = gdal.Open(filepath)
    col = dataset.RasterXSize#图像长度
    row = dataset.RasterYSize#图像宽度
    geotrans = dataset.GetGeoTransform()#读取仿射变换
    proj = dataset.GetProjection()#读取投影
    data = dataset.ReadAsArray()#转为numpy格式
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return [col, row, geotrans, proj, data]

def read_tif02(file):
    data = gdalnumeric.LoadFile(file)
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    data[data <= -3] = np.nan
    return data

def save_tif(data, file, output):
    ds = gdal.Open(file)
    shape = data.shape
    driver = gdal.GetDriverByName("GTiff")
    dataset = driver.Create(output, shape[1], shape[0], 1, gdal.GDT_Int16)#保存的数据类型
    dataset.SetGeoTransform(ds.GetGeoTransform())
    dataset.SetProjection(ds.GetProjection())
    dataset.GetRasterBand(1).WriteArray(data)

if __name__ == '__main__':
    file_path = r"D:\AAWORK\work\分段数据"
    file_out = r"D:\AAWORK\work\2021NDVISUM.tif"
    file_list = get_all_file_name(file_path)
    data_all = read_tif02(r"D:\AAWORK\work\研究方向\研究方向01作物分类内容\风云数据\MERSI-II植被指数旬产品(250M)\kongbai_zhu_250m.tif")
    for file in file_list:
        data = read_tif02(file)
        data_all = data_all+data
    save_tif(data_all,r"D:\AAWORK\work\研究方向\研究方向01作物分类内容\风云数据\MERSI-II植被指数旬产品(250M)\kongbai_zhu_250m.tif",file_out)

5 生成一个空白TIF(每个像元值为0的TIF)

思路比较简单,就是遍历每个像元,然后把每个像元的值设置为0,设置为其它可以,然后再进行保存。

from osgeo import gdal
import numpy as np

def read_tif(filepath):
    dataset = gdal.Open(filepath)
    col = dataset.RasterXSize#图像长度
    row = dataset.RasterYSize#图像宽度
    geotrans = dataset.GetGeoTransform()#读取仿射变换
    proj = dataset.GetProjection()#读取投影
    data = dataset.ReadAsArray()#转为numpy格式
    data = data.astype(np.float32)
    # a = data[0][0]
    # data[data == a] = np.nan
    # data[data <= -3] = np.nan
    return [col, row, geotrans, proj, data]

def save_tif(data, file, output):
    ds = gdal.Open(file)
    shape = data.shape
    driver = gdal.GetDriverByName("GTiff")
    dataset = driver.Create(output, shape[1], shape[0], 1, gdal.GDT_Int16)#保存的数据类型
    dataset.SetGeoTransform(ds.GetGeoTransform())
    dataset.SetProjection(ds.GetProjection())
    dataset.GetRasterBand(1).WriteArray(data)

if __name__ == '__main__':
    file_path = r"D:\AAWORK\work\2021NDVISUM.tif"
    file_out = r"D:\AAWORK\work\kongbai.tif"
    col, row, geotrans, proj, data = read_tif(file_path)
    for i in range(0,row):
        for j in range(0,col):
            data[i][j] = 0
    
    save_tif(data,file_path,file_out)

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