102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
'''
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https://blog.csdn.net/vonuo/article/details/74783291
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本方法是使用asd的方法来进行corning的辐亮度定标（resonon的方法感觉和此方法差不多）
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'''
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from osgeo import gdal
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import os, math
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import sys
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import easygui
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import numpy as np
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import pandas as pd
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import xlwt
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# 读写影像类
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class Grid(object):
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#读图像文件
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@classmethod
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def read_img(cls, filename, xoff=0, yoff=0, im_width=None, im_height=None):
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try:
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dataset = gdal.Open(filename) # 打开文件
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if im_width == None:
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im_width = dataset.RasterXSize # 栅格矩阵的列数
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if im_height == None:
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im_height = dataset.RasterYSize # 栅格矩阵的行数
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num_bands = dataset.RasterCount # 栅格矩阵的波段数
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im_geotrans = dataset.GetGeoTransform() # 仿射矩阵
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im_proj = dataset.GetProjection() # 地图投影信息
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im_data = dataset.ReadAsArray(xoff, yoff, im_width, im_height) # 将数据写成数组，对应栅格矩阵
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del dataset
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return im_proj, im_geotrans, im_data
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except:
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sys.exit()
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#写文件，以写成tif为例
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@classmethod
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def write_img(cls, dst_filename, data):
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format = "ENVI"
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driver = gdal.GetDriverByName(format)
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RasterXSize = data.shape[2] # 遥感影像的sample（列数）
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RasterYSize = data.shape[1] # 遥感影像的line（行数）
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band = data.shape[0]
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# driver.Create()函数中RasterXSize代表影像的sample（列数），RasterYSize代表影像的line（行数）
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dst_ds = driver.Create(dst_filename, RasterXSize, RasterYSize, band, gdal.GDT_Float32)
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for i in range(band):
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dst_ds.GetRasterBand(i + 1).WriteArray(data[i, :, :]) # gdal的band从1开始，所以dst_ds.GetRasterBand(i+1)
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dst_ds = None
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# 是否转辐亮度：0→不转，1→转
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rad_switch = 0
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img = r'D:\py_program\corning410\record_system_v24\baiban_record'
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img_baiban = r'D:\py_program\corning410\record_system_v24\baiban'
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img_gain = r'D:\py_program\corning410\corning410_radiance_calibration\jfq_dn_gain'
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img_offset = r'D:\py_program\corning410\record_system_v24\dark'
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dirpath = os.path.splitext(img)
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# 读取影像
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proj, geotrans, data = Grid.read_img(img)
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proj_baiban, geotrans_baiban, data_baiban = Grid.read_img(img_baiban)
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proj_gain, geotrans_gain, data_gain = Grid.read_img(img_gain)
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proj_offset, geotrans_offset, data_offset = Grid.read_img(img_offset)
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data_baiban = np.mean(data_baiban, axis=1)
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data_offset = np.mean(data_offset, axis=1)
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if rad_switch == 1:
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# 计算辐射定标参数
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cal_it = 6059
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target_it = 200004
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gain_scale = cal_it / target_it
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data_gain_adjust = data_gain * gain_scale
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# 影像和白板：1、扣除暗电流；2、转换成辐亮度；
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data_baiban = data_baiban - data_offset # 白板扣除暗电流
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data_baiban_rad = data_baiban * data_gain_adjust[:, 0, :] # 白板转辐亮度
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data_rad = np.empty(data.shape)
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for i in range(data.shape[1]):
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data_rad[:, i, :] = (data[:, i, :] - data_offset) * data_gain_adjust[:, 0, :] # 转辐亮度
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Grid.write_img(dirpath[0] + '_rad', data_rad)
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# 转换成反射率
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data_rad_ref = np.empty(data.shape)
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for i in range(data.shape[1]):
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data_rad_ref[:, i, :] = data_rad[:, i, :] / data_baiban_rad # 转反射率
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Grid.write_img(dirpath[0] + '_rad_ref', data_rad_ref)
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elif rad_switch == 0:
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# 影像和白板扣除暗电流
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data_baiban = data_baiban - data_offset
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data_rmdark = np.empty(data.shape)
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for i in range(data.shape[1]):
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data_rmdark[:, i, :] = (data[:, i, :] - data_offset)
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# 转反射率
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data_ref = np.empty(data.shape)
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for i in range(data.shape[1]):
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data_ref[:, i, :] = data_rmdark[:, i, :] / data_baiban # 转反射率
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Grid.write_img(dirpath[0] + '_ref', data_ref)
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