-
Notifications
You must be signed in to change notification settings - Fork 79
/
AtmosphericCorrection_Landsat8.py
275 lines (220 loc) · 10.9 KB
/
AtmosphericCorrection_Landsat8.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
#! 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
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)
# 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((9,2))
#计算辐射亮度参数
parameter_OLI[0,0] = float(''.join(re.findall('RADIANCE_MULT_BAND_1.+',data2)[0]).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[0,1] = float(''.join(re.findall('RADIANCE_ADD_BAND_1.+',data2)[0]).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])
Gain = parameter_OLI[int(BandId) - 1,0]
Bias = parameter_OLI[int(BandId) - 1,1]
RaCal = numpy.where(ImgRasterData>0 ,Gain * ImgRasterData + Bias,-9999)
return (RaCal)
# 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 == '1':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B1)
elif BandId == '2':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B2)
elif BandId == '3':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B3)
elif BandId == '4':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B4)
elif BandId == '5':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B5)
elif BandId == '6':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B6)
elif BandId == '7':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B7)
elif BandId == '8':
s.wavelength = Wavelength(PredefinedWavelengths.LANDSAT_OLI_B8)
elif BandId == '9':
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
return (xa, xb, xc)
if __name__ == '__main__':
#输入数据路径
RootInputPath = parse_arguments(sys.argv[1:]).Input_dir
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])
MeteDatas = glob.glob(os.path.join(root,'*MTL.txt'))
for MeteData in MeteDatas:
pass
f = open(MeteData)
data = f.readlines()
data2 =' '.join(data)
shutil.copyfile(MeteData,os.path.join(outname,os.path.basename(MeteData)))
if len(os.path.basename(MeteData))<10:
RSbands = glob.glob(os.path.join(root,"B0[1-8].tiff"))
else:
RSbands = glob.glob(os.path.join(root,"*B[1-8].TIF"))
print('影像'+root+'开始大气校正')
print(RSbands)
for tifFile in RSbands:
BandId = (os.path.basename(tifFile).split('.')[0])[-1]
#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)
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))
#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(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('第%s波段计算完成'%BandId)
# print(root+'计算完成')
print('\n')
#关闭日志文件
LogFile.close()