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AtmosphericCorrection_Sentinel.py
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AtmosphericCorrection_Sentinel.py
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#! usr/bin/env python
# -*- coding:utf-8 -*-
# created by zhaoguanhua 2017/10/9
# AtmosphericCorrection for Sentinel-2A
import glob
import os
import sys
import tarfile
import re
import numpy
from Py6S import *
from osgeo import gdal
from osgeo import osr
import xml.dom.minidom
import pdb
import shutil
from base import MeanDEM
import argparse
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)
def TOAReflectanceToTOARadiance(BandId):
'''
将表观反射率转换为表观辐射亮度
'''
global dom
#太阳辐照度
EsBand = numpy.zeros((14))
EsBand[1] = 1913.57
EsBand[2] = 1941.63
EsBand[3] = 1822.61
EsBand[4] = 1512.79
EsBand[5] = 1425.56
EsBand[6] = 1288.32
EsBand[7] = 1163.19
EsBand[8] = 1036.39
EsBand[9] = 955.19
EsBand[10]= 813.04
EsBand[11]= 367.15
EsBand[12]= 245.59
EsBand[13]= 85.25
#日地相对距离，在1左右波动，暂时用1代替
#公式：D=1+0.0167*sin(2*pi*(days-39.5)/360) days是儒略日
D = 1
#太阳天顶角
SunZenith = float(dom.getElementsByTagName('ZENITH_ANGLE')[0].firstChild.data)
#print('SunZenith=',SunZenith)
Us = numpy.cos(SunZenith/180*numpy.pi)
# y = numpy.where(RaCalRaster!=-9999,a * RaCalRaster - b,-9999)
# Radiance = (ImgRasterData/10000)*Us*EsBand[BandId]/(D*D*numpy.pi)
Radiance =numpy.where(ImgRasterData!=0,(ImgRasterData/10000)*Us*EsBand[BandId]/(D*D*numpy.pi),-9999)
return Radiance
# 6s大气校正
def AtmosphericCorrection(BandId):
'''
调用6s模型，给各参数赋值，得到大气校正参数
'''
global dom,SixsInputParameter
# 6S模型
s = SixS()
s.geometry = Geometry.User()
s.geometry.solar_z = SixsInputParameter["SolarZenithAngle"]
s.geometry.solar_a = SixsInputParameter["SolarAzimuthAngle"]
s.geometry.view_z = SixsInputParameter["ViewZenithAngle"][BandId]
s.geometry.view_a = SixsInputParameter["ViewAzimuthAngle"][BandId]
# 日期:月、日
s.geometry.month = SixsInputParameter["ImgMonth"]
s.geometry.day = SixsInputParameter["ImgDay"]
#大气模式类型
s.atmos_profile = AtmosProfile.PredefinedType(SixsInputParameter["AtmosphericProfile"])
# 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
# 研究区海拔、卫星传感器轨道高度
s.altitudes = Altitudes()
# s.altitudes.set_target_custom_altitude(0.015)
s.altitudes.set_target_custom_altitude(SixsInputParameter["meanDEM"])
s.altitudes.set_sensor_satellite_level()
#s.altitudes.set_sensor_custom_altitude(-705)
# 校正波段（根据波段名称）
if BandId == 1:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_01)
elif BandId == 2:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_02)
elif BandId == 3:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_03)
elif BandId == 4:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_04)
elif BandId == 5:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_05)
elif BandId == 6:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_06)
elif BandId == 7:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_07)
elif BandId == 8:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_08)
elif BandId == 9:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_09)
elif BandId == 10:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_10)
elif BandId == 11:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_11)
elif BandId == 12:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_12)
elif BandId == 13:
s.wavelength = Wavelength(PredefinedWavelengths.S2A_MSI_13)
# 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 BasicParameters():
'''
获取6s大气校正所需的参数
'''
global dom
SixsParameters = dict()
#太阳天顶角、方位角
SunAngle = dom.getElementsByTagName('Mean_Sun_Angle')
SixsParameters["SolarZenithAngle"] = float(SunAngle[0].getElementsByTagName('ZENITH_ANGLE')[0].firstChild.data)
SixsParameters["SolarAzimuthAngle"] = float(SunAngle[0].getElementsByTagName('AZIMUTH_ANGLE')[0].firstChild.data)
#卫星天顶角、方位角
ViewAngles = dom.getElementsByTagName('Mean_Viewing_Incidence_Angle')
ViewZeniths = dict()
ViewAzimuths = dict()
for angle in ViewAngles:
ViewAngle = int(angle.getAttribute('bandId'))
#print(ViewAngle)
ViewZeniths[ViewAngle+1] = float(angle.getElementsByTagName('ZENITH_ANGLE')[0].firstChild.data)
ViewAzimuths[ViewAngle+1]= float(angle.getElementsByTagName('AZIMUTH_ANGLE')[0].firstChild.data)
SixsParameters["ViewZenithAngle"] = ViewZeniths
SixsParameters["ViewAzimuthAngle"] = ViewAzimuths
# 日期:月、日
Date = dom.getElementsByTagName('SENSING_TIME')[0].firstChild.data.split('T')[0]
SixsParameters["ImgMonth"] = int(Date.split('-')[1])
SixsParameters["ImgDay"] = int(Date.split('-')[2])
#求影像中心经纬度
PointULX = int(dom.getElementsByTagName('ULX')[0].firstChild.data)
PointULY = int(dom.getElementsByTagName('ULY')[0].firstChild.data)
Imgsizes = dom.getElementsByTagName('Size')
for Imgsize in Imgsizes:
Resolution = Imgsize.getAttribute('resolution')
if Resolution == '10':
SixsParameters["Nrows"] = int(Imgsize.getElementsByTagName('NROWS')[0].firstChild.data)
SixsParameters["Ncols"] = int(Imgsize.getElementsByTagName('NCOLS')[0].firstChild.data)
PointBRX = PointULX + 10*SixsParameters["Ncols"]
PointBRY = PointULY - 10*SixsParameters["Nrows"]
# 将投影坐标转为经纬度坐标（具体的投影坐标系由给定数据确定）
Proj = dom.getElementsByTagName('HORIZONTAL_CS_CODE')[0].firstChild.data
ProjCode = int(Proj.split(':')[1])
source = osr.SpatialReference()
source.ImportFromEPSG(ProjCode)
target = osr.SpatialReference()
target.ImportFromEPSG(4326)
ct = osr.CoordinateTransformation(source,target)
CoordsUL,CoordsBR = ct.TransformPoints([(PointULX,PointULY),(PointBRX,PointBRY)])
ULLat = CoordsUL[0]
ULLon = CoordsUL[1]
BRLat = CoordsBR[0]
BRLon = CoordsBR[1]
sLongitude = (ULLon+BRLon) / 2
sLatitude = (ULLat+BRLat) / 2
#大气模式类型
if sLatitude > -15 and sLatitude <= 15:
SixsParameters["AtmosphericProfile"] = 1 #Tropical
elif sLatitude > 15 and sLatitude <= 45:
if SixsParameters["ImgMonth"] > 4 and SixsParameters["ImgMonth"] <= 9:
SixsParameters["AtmosphericProfile"] = 2 #MidlatitudeSummer
else:
SixsParameters["AtmosphericProfile"] = 3 #MidlatitudeWinter
elif sLatitude > 45 and sLatitude <= 60:
if SixsParameters["ImgMonth"] > 4 and SixsParameters["ImgMonth"] <= 9:
SixsParameters["AtmosphericProfile"] = 4 #SubarctivWinter
else:
SixsParameters["AtmosphericProfile"] = 5 #SubarcticWinter
pointUL = dict()
pointDR = dict()
pointUL["lat"] = ULLat
pointUL["lon"] = ULLon
pointDR["lat"] = BRLat
pointDR["lon"] = BRLon
SixsParameters["meanDEM"] = (MeanDEM(pointUL, pointDR)) * 0.001
return SixsParameters
def AWS_file_bk():
pass
# #输入数据路径
# RootInputPath = parse_arguments(sys.argv[1:]).Input_dir
# #输出路径
# RootOutName = parse_arguments(sys.argv[2:]).Output_dir
#
# #创建日志文件
# LogFile = open(os.path.join(RootOutName,'log.txt'),'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(os.path.sep)
# RootList = root.split(os.path.sep)
# 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])
#
# #获得影像头文件
# MeteData = os.path.join(root,'metadata.xml')
# print(MeteData)
# shutil.copyfile(MeteData,os.path.join(outname,'metedata.xml'))
# dom = xml.dom.minidom.parse(MeteData)
# SixsInputParameter = BasicParameters()
#
# #选出影像所有波段
# RSbands = glob.glob(os.path.join(root,"B*.tiff"))
#
# for tifFile in RSbands:
# print(tifFile)
# if os.path.basename(tifFile)=="B8A.tiff":
# BandId = 9
# elif int(os.path.basename(tifFile)[1:3])<9:
# BandId = int(os.path.basename(tifFile)[1:3])
# else:
# BandId = int(os.path.basename(tifFile)[1:3])+1
# print(BandId)
# #捕捉打开数据出错异常
# try:
# IDataSet = gdal.Open(tifFile)
# except Exception as e:
# print("文件%S打开失败" % tifFile)
# LogFile.write('\n'+tifFile+'数据打开失败')
#
# if IDataSet == None:
# LogFile.write('\n'+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)
# # print(ImgRasterData)
# if ImgRasterData is None:
# LogFile.write('\n'+tifFile+'栅格数据为空')
# continue
# else:
# #设置输出文件路径
# outFilename=os.path.join(outname,os.path.basename(tifFile)[0:3]+'.tiff')
# # print(outFilename)
#
# #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:
# #表观反射率转换为辐射亮度值
# RaCalRaster = TOAReflectanceToTOARadiance(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 = gdal.GetDriverByName('GTIFF')
# #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)
if __name__ == '__main__':
#获得影像头文件
# file_path =r"D:\test\L1C_T51TUE_A004877_20180211T025320"
# # output_file=r"D:\test\ac_s2"
#输入数据路径
file_path = parse_arguments(sys.argv[1:]).Input_dir
#输出路径
output_file = parse_arguments(sys.argv[2:]).Output_dir
MeteData = os.path.join(file_path, 'MTD_TL.xml')
#print(MeteData)
dom = xml.dom.minidom.parse(MeteData)
SixsInputParameter = BasicParameters()
# 选出影像所有波段
RSbands = glob.glob(os.path.join(file_path,"IMG_DATA","*B*.jp2"))
for imgFile in RSbands:
img_basename = os.path.basename(imgFile)
band_id = img_basename[-6:-4]
if band_id == "8A":
BandId = 9
elif int(band_id) < 9:
BandId = int(band_id)
else:
BandId = int(band_id) + 1
# 捕捉打开数据出错异常
try:
IDataSet = gdal.Open(imgFile)
except Exception as e:
print("文件{file}打开失败".format(file=imgFile))
if IDataSet == None:
print("{file}数据集读取为空".format(file=imgFile))
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:
print("{file}栅格数据为空".format(file=imgFile))
continue
else:
# 设置输出文件路径
outFilename = os.path.join(output_file,img_basename.replace(".jp2",".tiff"))
# 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:
# 表观反射率转换为辐射亮度值
RaCalRaster = TOAReflectanceToTOARadiance(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 = gdal.GetDriverByName('GTIFF')
# 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('{file}计算完成'.format(file=img_basename))