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AtmosphericCorrection_GF.py
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AtmosphericCorrection_GF.py
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#! usr/bin/env python
# -*- coding:utf-8 -*-
# created by zhaoguanhua 2017/09/04
import glob
import os
import sys
import tarfile #解压缩
import json
import numpy as np
import gdal
import pdb
import math
import time
import xml.dom.minidom #读取xml格式的影像头文件
from tqdm import tqdm #进度条
from Py6S import *
import argparse
from base import MeanDEM
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--Input_dir',type=str,help='Input dir',default=None)
parser.add_argument('--Output_dir',type=str,help='Output dir',default=None)
return parser.parse_args(argv)
# 解压缩原始文件
def untar(fname, dirs):
print("文件路径",fname)
try:
t = tarfile.open(fname)
except Exception as e:
print("文件%s打开失败" % fname)
t.extractall(path=dirs)
def Block(IDataSet):
global cols,rows,atcfiles
#设置输出波段
Driver = IDataSet.GetDriver()
geoTransform1 = IDataSet.GetGeoTransform()
ListgeoTransform1 = list(geoTransform1)
ListgeoTransform1[5] = -ListgeoTransform1[5]
newgeoTransform1 = tuple(ListgeoTransform1)
proj1 = IDataSet.GetProjection()
OutRCname = os.path.join(atcfiles,outFileName+".tif")
outDataset = Driver.Create(OutRCname,cols,rows,4,gdal.GDT_Int32)
outDataset.SetGeoTransform(newgeoTransform1)
outDataset.SetProjection(proj1)
#分别读取4个波段
for m in range(1,5):
ReadBand = IDataSet.GetRasterBand(m)
outband = outDataset.GetRasterBand(m)
outband.SetNoDataValue(-9999)
#获取对应波段的增益gain和偏移bias
Gain,Bias = RadiometricCalibration(m)
#获取大气校正系数
AtcCofa, AtcCofb, AtcCofc = AtmosphericCorrection(m)
nBlockSize = 1024
i = 0
j = 0
b = cols*rows
#进度条参数
XBlockcount = math.ceil(cols/nBlockSize)
YBlockcount = math.ceil(rows/nBlockSize)
print("第%d波段校正："%m)
try:
with tqdm(total=XBlockcount*YBlockcount,iterable='iterable',desc = '第%i波段:'%m) as pbar:
# with tqdm(total=XBlockcount*YBlockcount) as pbar:
# print(pbar)
while i<rows:
while j <cols:
#保存分块大小
nXBK = nBlockSize
nYBK = nBlockSize
#最后不够分块的区域，有多少读取多少
if i+nBlockSize>rows:
nYBK = rows - i
if j+nBlockSize>cols:
nXBK=cols - j
#分块读取影像
Image = ReadBand.ReadAsArray(j,i,nXBK,nYBK)
outImage =np.where(Image>0,Image*Gain + Bias,-9999)
y = np.where(outImage!=-9999,AtcCofa * outImage - AtcCofb,-9999)
atcImage = np.where(y!=-9999,(y / (1 + y * AtcCofc))*10000,-9999)
outband.WriteArray(atcImage,j,i)
j=j+nXBK
time.sleep(1)
pbar.update(1)
j=0
i=i+nYBK
except KeyboardInterrupt:
pbar.close()
raise
pbar.close()
def RadiometricCalibration(BandId):
global cols,rows,SatelliteID,SensorID,Year,ImageType,config
if SensorID[0:3] == "WFV":
Gain_ =config["Parameter"][SatelliteID][SensorID][Year]["gain"][BandId-1]
Bias_ =config["Parameter"][SatelliteID][SensorID][Year]["offset"][BandId-1]
else:
Gain_ =config["Parameter"][SatelliteID][SensorID][Year][ImageType]["gain"][BandId-1]
Bias_ =config["Parameter"][SatelliteID][SensorID][Year][ImageType]["offset"][BandId-1]
return Gain_,Bias_
# 6s大气校正
def AtmosphericCorrection(BandId):
global metedata,config,SatelliteID,SensorID
#读取头文件
dom = xml.dom.minidom.parse(metedata)
# 6S模型
s = SixS()
# 传感器类型 自定义
s.geometry = Geometry.User()
s.geometry.solar_z = 90-float(dom.getElementsByTagName('SolarZenith')[0].firstChild.data)
s.geometry.solar_a = float(dom.getElementsByTagName('SolarAzimuth')[0].firstChild.data)
# s.geometry.view_z = float(dom.getElementsByTagName('SatelliteZenith')[0].firstChild.data)
# s.geometry.view_a = float(dom.getElementsByTagName('SatelliteAzimuth')[0].firstChild.data)
s.geometry.view_z = 0
s.geometry.view_a = 0
# 日期
DateTimeparm = dom.getElementsByTagName('CenterTime')[0].firstChild.data
DateTime = DateTimeparm.split(' ')
Date = DateTime[0].split('-')
s.geometry.month = int(Date[1])
s.geometry.day = int(Date[2])
# print(s.geometry)
# 中心经纬度
TopLeftLat = float(dom.getElementsByTagName('TopLeftLatitude')[0].firstChild.data)
TopLeftLon = float(dom.getElementsByTagName('TopLeftLongitude')[0].firstChild.data)
TopRightLat = float(dom.getElementsByTagName('TopRightLatitude')[0].firstChild.data)
TopRightLon = float(dom.getElementsByTagName('TopRightLongitude')[0].firstChild.data)
BottomRightLat = float(dom.getElementsByTagName('BottomRightLatitude')[0].firstChild.data)
BottomRightLon = float(dom.getElementsByTagName('BottomRightLongitude')[0].firstChild.data)
BottomLeftLat = float(dom.getElementsByTagName('BottomLeftLatitude')[0].firstChild.data)
BottomLeftLon = float(dom.getElementsByTagName('BottomLeftLongitude')[0].firstChild.data)
ImageCenterLat = (TopLeftLat + TopRightLat + BottomRightLat + BottomLeftLat) / 4
# 大气模式类型
if ImageCenterLat > -15 and ImageCenterLat < 15:
s.atmos_profile = AtmosProfile.PredefinedType(AtmosProfile.Tropical)
if ImageCenterLat > 15 and ImageCenterLat < 45:
if s.geometry.month > 4 and s.geometry.month < 9:
s.atmos_profile = AtmosProfile.PredefinedType(AtmosProfile.MidlatitudeSummer)
else:
s.atmos_profile = AtmosProfile.PredefinedType(AtmosProfile.MidlatitudeWinter)
if ImageCenterLat > 45 and ImageCenterLat < 60:
if s.geometry.month > 4 and s.geometry.month < 9:
s.atmos_profile = AtmosProfile.PredefinedType(AtmosProfile.SubarcticSummer)
else:
s.atmos_profile = AtmosProfile.PredefinedType(AtmosProfile.SubarcticWinter)
# 气溶胶类型大陆
s.aero_profile = AtmosProfile.PredefinedType(AeroProfile.Continental)
# 下垫面类型
s.ground_reflectance = GroundReflectance.HomogeneousLambertian(0.36)
# 550nm气溶胶光学厚度,对应能见度为40km
s.aot550 = 0.14497
# 通过研究去区的范围去求DEM高度。
pointUL = dict()
pointDR = dict()
pointUL["lat"] = max(TopLeftLat,TopRightLat,BottomRightLat,BottomLeftLat)
pointUL["lon"] = min(TopLeftLon,TopRightLon,BottomRightLon,BottomLeftLon)
pointDR["lat"] = min(TopLeftLat,TopRightLat,BottomRightLat,BottomLeftLat)
pointDR["lon"] = max(TopLeftLon,TopRightLon,BottomRightLon,BottomLeftLon)
meanDEM = (MeanDEM(pointUL, pointDR)) * 0.001
# 研究区海拔、卫星传感器轨道高度
s.altitudes = Altitudes()
s.altitudes.set_target_custom_altitude(meanDEM)
s.altitudes.set_sensor_satellite_level()
# 校正波段（根据波段名称）
if BandId == 1:
SRFband = config["Parameter"][SatelliteID][SensorID]["SRF"]["1"]
s.wavelength = Wavelength(0.450,0.520,SRFband)
elif BandId == 2:
SRFband = config["Parameter"][SatelliteID][SensorID]["SRF"]["2"]
s.wavelength = Wavelength(0.520,0.590,SRFband)
elif BandId == 3:
SRFband = config["Parameter"][SatelliteID][SensorID]["SRF"]["3"]
s.wavelength = Wavelength(0.630,0.690,SRFband)
elif BandId == 4:
SRFband = config["Parameter"][SatelliteID][SensorID]["SRF"]["4"]
s.wavelength = Wavelength(0.770,0.890,SRFband)
s.atmos_corr = AtmosCorr.AtmosCorrLambertianFromReflectance(-0.1)
# 运行6s大气模型
s.run()
xa = s.outputs.coef_xa
xb = s.outputs.coef_xb
xc = s.outputs.coef_xc
# x = s.outputs.values
return (xa, xb, xc)
if __name__ == '__main__':
script_path = os.path.split(os.path.realpath(__file__))[0]
#读取辐射校正和大气校正所需参数:增益、偏移和光谱响应函数
config_file = os.path.join(script_path,"RadiometricCorrectionParameter.json")
config = json.load(open(config_file))
#输入数据路径
InputFilePath = parse_arguments(sys.argv[1:]).Input_dir
OutputFilePath = parse_arguments(sys.argv[2:]).Output_dir
for root,dirs,tarFiles in os.walk(InputFilePath):
pass
for tarFile in tarFiles:
print(tarFile)
filename = os.path.basename(tarFile)
fileType = filename[0:2]
filename_split = filename.split("_")
if fileType == 'GF':
#GFType = filename[4:7]
GFType = filename_split[1][:3]
intputname = os.path.join(InputFilePath,filename)
outFileName = filename[:-7]
outname = os.path.join(InputFilePath,outFileName)
atcfiles = os.path.join(OutputFilePath,outFileName)
print("文件"+filename+"开始解压缩")
try:
untar(intputname, outname)
except Exception as e:
continue
if GFType == 'WFV':
# tiffFile = glob.glob(outname + "/*.tiff")[0]
# metedata = glob.glob(outname+"/*.xml")[0]
tiffFile = glob.glob(os.path.join(outname,"*.tiff"))[0]
metedata = glob.glob(os.path.join(outname,"*.xml"))[0]
elif GFType == 'PMS':
# tiffFile = glob.glob(outname + "/*mss*.tiff")[0]
# metedata = glob.glob(outname+"/*mss*.xml")[0]
tiffFile = glob.glob(os.path.join(outname,"*MSS*.tiff"))[0]
metedata = glob.glob(os.path.join(outname,"*MSS*.xml"))[0]
try:
os.mkdir(atcfiles)
except Exception as e:
pass
print(filename+"解压缩完成")
try:
IDataSet = gdal.Open(tiffFile)
except Exception as e:
print("文件%S打开失败" % tiffFile)
cols = IDataSet.RasterXSize
rows = IDataSet.RasterYSize
# SatelliteID = filename[0:3]
# SensorID = filename[4:8]
# Year = filename[22:26]
SatelliteID = filename_split[0]
SensorID = filename_split[1]
Year = filename_split[4][:4]
ImageType =os.path.basename(tiffFile)[-9:-6]
Block(IDataSet)