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AtmosphericCorrection.py
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AtmosphericCorrection.py
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#! usr/bin/env python
# -*- coding:utf-8 -*-
# created by zhaoguanhua 2017/9/25
# AtmosphericCorrection for Landsat8
import glob
import os
import sys
import tarfile
import re
import gdal
import numpy
from Py6S import *
from osgeo import gdal
import pdb
import shutil
# 解压缩原始文件
def untar(fname, dirs):
try:
t = tarfile.open(fname)
except Exception as e:
print("文件%s打开失败" % fname)
t.extractall(path=dirs)
# 逐波段辐射定标
def RadiometricCalibration(BandId):
# LandSat8 TM辐射定标参数
global data2,ImgRasterData
parameter_OLI = numpy.zeros((11,2))
#计算辐射亮度参数
# parameter_OLI[0,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_1.+',data2)).split("=")[1])
parameter_OLI[1,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_2.+',data2)).split("=")[1])
parameter_OLI[2,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_3.+',data2)).split("=")[1])
parameter_OLI[3,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_4.+',data2)).split("=")[1])
parameter_OLI[4,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_5.+',data2)).split("=")[1])
parameter_OLI[5,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_6.+',data2)).split("=")[1])
parameter_OLI[6,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_7.+',data2)).split("=")[1])
parameter_OLI[7,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_8.+',data2)).split("=")[1])
parameter_OLI[8,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_9.+',data2)).split("=")[1])
parameter_OLI[9,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_10.+',data2)).split("=")[1])
parameter_OLI[10,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_11.+',data2)).split("=")[1])
# parameter_OLI[0,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_1.+',data2)).split("=")[1])
parameter_OLI[1,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_2.+',data2)).split("=")[1])
parameter_OLI[2,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_3.+',data2)).split("=")[1])
parameter_OLI[3,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_4.+',data2)).split("=")[1])
parameter_OLI[4,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_5.+',data2)).split("=")[1])
parameter_OLI[5,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_6.+',data2)).split("=")[1])
parameter_OLI[6,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_7.+',data2)).split("=")[1])
parameter_OLI[7,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_8.+',data2)).split("=")[1])
parameter_OLI[8,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_9.+',data2)).split("=")[1])
parameter_OLI[9,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_10.+',data2)).split("=")[1])
parameter_OLI[10,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_11.+',data2)).split("=")[1])
if len(BandId) ==8:
n = int(BandId[2])
else:
n = int(BandId[1:3])
Gain = parameter_OLI[n - 1,0]
Bias = parameter_OLI[n - 1,1]
RaCal = numpy.where(ImgRasterData>0 ,Gain * ImgRasterData + Bias,-9999)
return (RaCal)
#计算表观反射率
def TOAReflectance(BandId):
# LandSat8 TM辐射定标参数
global data2,ImgRasterData
parameter_OLI = numpy.zeros((9,2))
#计算表观反射率参数
parameter_OLI[0,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_1.+',data2)).split("=")[1])
parameter_OLI[1,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_2.+',data2)).split("=")[1])
parameter_OLI[2,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_3.+',data2)).split("=")[1])
parameter_OLI[3,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_4.+',data2)).split("=")[1])
parameter_OLI[4,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_5.+',data2)).split("=")[1])
parameter_OLI[5,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_6.+',data2)).split("=")[1])
parameter_OLI[6,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_7.+',data2)).split("=")[1])
parameter_OLI[7,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_8.+',data2)).split("=")[1])
parameter_OLI[8,0] = float(''.join(re.findall('REFLECTANCE_MULT_BAND_9.+',data2)).split("=")[1])
parameter_OLI[0,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_1.+',data2)).split("=")[1])
parameter_OLI[1,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_2.+',data2)).split("=")[1])
parameter_OLI[2,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_3.+',data2)).split("=")[1])
parameter_OLI[3,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_4.+',data2)).split("=")[1])
parameter_OLI[4,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_5.+',data2)).split("=")[1])
parameter_OLI[5,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_6.+',data2)).split("=")[1])
parameter_OLI[6,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_7.+',data2)).split("=")[1])
parameter_OLI[7,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_8.+',data2)).split("=")[1])
parameter_OLI[8,1] = float(''.join(re.findall('REFLECTANCE_ADD_BAND_9.+',data2)).split("=")[1])
n = int(BandId[1])
Gain = parameter_OLI[n - 1,0]
Bias = parameter_OLI[n - 1,1]
SunElevationFactor = numpy.sin(float(''.join(re.findall('SUN_ELEVATION.+',data2)).split("=")[1])/180*numpy.pi)
TOARef = numpy.where(ImgRasterData>0,(Gain * ImgRasterData + Bias)/SunElevationFactor,-9999)
return (TOARef)
# 6s大气校正
def AtmosphericCorrection(BandId):
global data
# 6S模型
s = SixS()
s.geometry = Geometry.User()
s.geometry.solar_z = 90-float(''.join(re.findall('SUN_ELEVATION.+',data2)).split("=")[1])
s.geometry.solar_a = float(''.join(re.findall('SUN_AZIMUTH.+',data2)).split("=")[1])
s.geometry.view_z = 0
s.geometry.view_a = 0
# 日期
Dateparm = ''.join(re.findall('DATE_ACQUIRED.+',data2)).split("=")
Date = Dateparm[1].split('-')
s.geometry.month = int(Date[1])
s.geometry.day = int(Date[2])
# 中心经纬度
point1lat = float(''.join(re.findall('CORNER_UL_LAT_PRODUCT.+',data2)).split("=")[1])
point1lon = float(''.join(re.findall('CORNER_UL_LON_PRODUCT.+',data2)).split("=")[1])
point2lat = float(''.join(re.findall('CORNER_UR_LAT_PRODUCT.+',data2)).split("=")[1])
point2lon = float(''.join(re.findall('CORNER_UR_LON_PRODUCT.+',data2)).split("=")[1])
point3lat = float(''.join(re.findall('CORNER_LL_LAT_PRODUCT.+',data2)).split("=")[1])
point3lon = float(''.join(re.findall('CORNER_LL_LON_PRODUCT.+',data2)).split("=")[1])
point4lat = float(''.join(re.findall('CORNER_LR_LAT_PRODUCT.+',data2)).split("=")[1])
point4lon = float(''.join(re.findall('CORNER_LR_LON_PRODUCT.+',data2)).split("=")[1])
sLongitude = (point1lon + point2lon + point3lon + point4lon) / 4
sLatitude = (point1lat + point2lat + point3lat + point4lat) / 4
# 大气模式类型
if sLatitude > -15 and sLatitude <= 15:
s.atmos_profile = AtmosProfile.PredefinedType(AtmosProfile.Tropical)
if sLatitude > 15 and sLatitude <= 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 sLatitude > 45 and sLatitude <= 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)
# 目标地物？？？？？？
s.ground_reflectance = GroundReflectance.HomogeneousLambertian(0.36)
# 550nm气溶胶光学厚度,根据日期从MODIS处获取。
#s.visibility=40.0
s.aot550 = 0.14497
# 通过研究去区的范围去求DEM高度。
pointUL = dict()
pointDR = dict()
pointUL["lat"] = point1lat
pointUL["lon"] = point1lon
pointDR["lat"] = point4lat
pointDR["lon"] = point2lon
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 == 'B1.TIF':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B1)
elif BandId == 'B2.TIF':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B2)
elif BandId == 'B03.tiff':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B3)
elif BandId == 'B04.tiff':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B4)
elif BandId == 'B05.tiff':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B5)
elif BandId == 'B6.TIF':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B6)
elif BandId == 'B7.TIF':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B7)
elif BandId == 'B8.TIF':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B8)
elif BandId == 'B9.TIF':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B9)
# 下垫面非均一、朗伯体
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
print(x)
return (xa, xb, xc)
def MeanDEM(pointUL, pointDR):
# 打开DEM数据
try:
DEMIDataSet = gdal.Open("GMTED2km.tif")
except Exception as e:
pass
DEMBand = DEMIDataSet.GetRasterBand(1)
cols = DEMIDataSet.RasterXSize
rows = DEMIDataSet.RasterYSize
geotransform = DEMIDataSet.GetGeoTransform()
# DEM分辨率
pixelWidth = geotransform[1]
pixelHight = geotransform[5]
# DEM起始点：左上角，X：经度，Y：纬度
originX = geotransform[0]
originY = geotransform[3]
# 研究区左上角在矩阵中的位置
yoffset1 = int((originY - pointUL['lat']) / pixelWidth)
xoffset1 = int((pointUL['lon'] - originX) / (-pixelHight))
# 研究区右下角在矩阵中的位置
yoffset2 = int((originY - pointDR['lat']) / pixelWidth)
xoffset2 = int((pointDR['lon'] - originX) / (-pixelHight))
# 研究区矩阵行列数
xx = xoffset2 - xoffset1
yy = yoffset2 - yoffset1
# 读取研究区内的数据，并计算高程
DEMRasterData = DEMBand.ReadAsArray(xoffset1, yoffset1, xx, yy)
MeanAltitude = numpy.mean(DEMRasterData)
return MeanAltitude
def CloudMaskScore():
mask = BrightTemp == -9999
#得分1：Blue
BluePart = numpy.ma.array((TOARefRasterBlue-0.1)/0.2,mask=mask)
BluePart.fill_value=-9999
ScorePart1 = numpy.where(BluePart.filled()>1,1,BluePart.filled())
#得分2：Red、Blue、Green
RGBPart = numpy.ma.array((TOARefRasterBlue+TOARefRasterGreen+TOARefRasterRed-0.2)/0.6,mask=mask)
RGBPart.fill_value=-9999
ScorePart2 = numpy.where(RGBPart.filled()>ScorePart1,ScorePart1,RGBPart.filled())
#得分3：Nir、Swir1、Swir2
NSSPart = numpy.ma.array((TOARefRasterNir+TOARefRasterSwir1+TOARefRasterSwir2-0.3)/0.5,mask=mask)
NSSPart.fill_value=-9999
ScorePart3 = numpy.where(NSSPart.filled()>ScorePart2,ScorePart2,NSSPart.filled())
#得分4:temperature
TempPart = numpy.ma.array((BrightTemp-300)/(-10),mask=mask)
TempPart.fill_value=-9999
ScorePart4 = numpy.where(TempPart.filled()>ScorePart3,ScorePart3,TempPart.filled())
#得分5NDSI：Green、TOARefRasterSwir1
NDSIPart1 = numpy.ma.array((TOARefRasterGreen- TOARefRasterSwir1)/(TOARefRasterGreen+TOARefRasterSwir1),mask=mask)
NDSIPart2 = numpy.ma.array((NDSIPart1-0.8)/(-0.2),mask=mask)
NDSIPart2.fill_value=-9999
ScorePart5 = numpy.where(NDSIPart2.filled()>ScorePart4,ScorePart4,NDSIPart2.filled())
ScoreCloud = numpy.where(ScorePart5!=-9999,1- ScorePart5,-9999)
return ScoreCloud
if __name__ == '__main__':
#输入数据路径
RootOutName = sys.argv[2]
RootInputPath = sys.argv[1]
Contronal=0
#创建日志文件
LogFile = open(os.path.join(RootOutName,'log.text'),'w')
for root,dirs,RSFiles in os.walk(RootInputPath):
#判断是否进入最底层
if len(dirs)==0:
#根据输入输出路径建立生成新文件的路径
RootInputPathList = RootInputPath.split('/')
RootList = root.split('/')
StartList = len(RootInputPathList)
EndList = len(RootList)
outname = RootOutName
for i in range(StartList,EndList):
if os.path.exists(os.path.join(outname,RootList[i]))==False:
os.makedirs(os.path.join(outname,RootList[i]))
outname=os.path.join(outname,RootList[i])
else:
outname=os.path.join(outname,RootList[i])
#判断文件是否都存在
CloudScoreFile = os.path.join(outname,RootList[-1]+'_CloudScore.TIF')
if os.path.isfile(CloudScoreFile):
print(root+'计算完成')
continue
else:
MeteData = os.path.join(root,'MTL.txt')
f = open(MeteData)
data = f.readlines()
data2 =' '.join(data)
shutil.copyfile(MeteData,os.path.join(outname,RootList[-1]+'MTL.txt'))
for tifFile in RSFiles:
# print(tifFile)
if tifFile[-5:] == '.tiff':
BandId = (os.path.basename(tifFile))
# print(BandId)
#捕捉打开数据出错异常
try:
IDataSet = gdal.Open(os.path.join(root,tifFile))
except Exception as e:
print("文件%S打开失败" % tifFile)
LogFile.write('\n'+os.path.join(root,tifFile)+'数据打开失败')
if IDataSet == None:
LogFile.write('\n'+os.path.join(root,tifFile)+'数据集读取为空')
continue
else:
#获取行列号
cols = IDataSet.RasterXSize
rows = IDataSet.RasterYSize
ImgBand = IDataSet.GetRasterBand(1)
ImgRasterData = ImgBand.ReadAsArray(0, 0, cols, rows)
if ImgRasterData is None:
LogFile.write('\n'+os.path.join(root,tifFile)+'栅格数据为空')
continue
else:
if BandId =='B02.tiff':
# TOARefRasterBlue = TOAReflectance(BandId)
RaCalRaster = RadiometricCalibration(BandId)
Contronal = Contronal + 1
elif BandId =='B03.tiff':
# TOARefRasterGreen = TOAReflectance(BandId)
RaCalRaster = RadiometricCalibration(BandId)
Contronal = Contronal + 1
elif BandId =='B04.tiff':
# TOARefRasterRed = TOAReflectance(BandId)
RaCalRaster = RadiometricCalibration(BandId)
Contronal = Contronal + 1
elif BandId =='B05.tiff':
# TOARefRasterNir = TOAReflectance(BandId)
RaCalRaster = RadiometricCalibration(BandId)
Contronal = Contronal + 1
# elif BandId =='B6.TIF':
# TOARefRasterSwir1 = TOAReflectance(BandId)
# Contronal = Contronal + 1
# elif BandId =='B7.TIF':
# TOARefRasterSwir2 = TOAReflectance(BandId)
# Contronal = Contronal + 1
# elif tifFile[-7:] =='B10.TIF':
# RaCalRaster = RadiometricCalibration(BandId)
# Contronal = Contronal + 1
# BrightTemp = numpy.where(RaCalRaster!=-9999,1321.08/numpy.log(774.89/RaCalRaster+1),-9999)
# print("亮温计算完成")
if BandId == 'B02.tiff'or BandId == 'B03.tiff'or BandId == 'B04.tiff'or BandId == 'B05.tiff':
#设置输出文件路径
outFilename=os.path.join(outname,os.path.basename(tifFile))
#如果文件存在就跳过，进行下一波段操作
if os.path.isfile(outFilename):
print("%s已经完成" % outFilename)
continue
else:
# #辐射校正
# RaCalRaster = RadiometricCalibration(tifFile, BandId)
#大气校正
a, b, c = AtmosphericCorrection(BandId)
y = numpy.where(RaCalRaster!=-9999,a * RaCalRaster - b,-9999)
atc = numpy.where(y!=-9999,(y / (1 + y * c))*10000,-9999)
driver = IDataSet.GetDriver()
#输出栅格数据集
outDataset = driver.Create(outFilename, cols, rows, 1, gdal.GDT_Int16)
# 设置投影信息，与原数据一样
geoTransform = IDataSet.GetGeoTransform()
outDataset.SetGeoTransform(geoTransform)
proj = IDataSet.GetProjection()
outDataset.SetProjection(proj)
outband = outDataset.GetRasterBand(1)
outband.SetNoDataValue(-9999)
outband.WriteArray(atc, 0, 0)
print(root+'计算完成')
# if Contronal == 7:
# print(Contronal)
# #设置输出文件路径
# outFilename=os.path.join(outname,os.path.basename(tifFile)[0:41]+'CloudScore.TIF')
# CloudScoreFile = os.path.join(outname,RootList[-1]+'_CloudScore.TIF')
# CloudScoreData = CloudMaskScore()
# driver = IDataSet.GetDriver()
# #输出栅格数据集
# CloudDataset = driver.Create(CloudScoreFile, cols, rows, 1, gdal.GDT_Float32)
# # 设置投影信息，与原数据一样
# geoTransform = IDataSet.GetGeoTransform()
# proj = IDataSet.GetProjection()
# CloudDataset.SetGeoTransform(geoTransform)
# CloudDataset.SetProjection(proj)
# outband = CloudDataset.GetRasterBand(1)
# outband.SetNoDataValue(-9999)
# outband.WriteArray(CloudScoreData, 0, 0)
# print('影像'+outFilename + '处理完成')
# RasterData = None
# Contronal=0
# else:
# Contronal=0
#关闭日志文件
LogFile.close()