遥感影像16位转8位(python)

遥感影像16位转8位

项目中需要对遥感影像先做类型转换，将16位转成8位，结合之前的文章灰度级压缩，同样使用累计直方图的方法，对多光谱图像进行逐波段的压缩。

#!usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author  : 
@Email   : 
@Time    : 14:36
@Site    :
@File    : CompressImage.py
@Software: PyCharm
"""

"""
将16位遥感图像压缩至8位，并保持色彩一致
"""

import  gdal
import os
import glob
import numpy as np

def read_tiff(input_file):
    """
    读取影像
    :param input_file:输入影像
    :return:波段数据，仿射变换参数，投影信息、行数、列数、波段数
    """

    dataset = gdal.Open(input_file)
    rows = dataset.RasterYSize
    cols = dataset.RasterXSize

    geo = dataset.GetGeoTransform()
    proj = dataset.GetProjection()

    couts = dataset.RasterCount

    array_data = np.zeros((couts,rows,cols))

    for i in range(couts):
        band = dataset.GetRasterBand(i+1)
        array_data[i,:,:] = band.ReadAsArray()


    return array_data,geo,proj,rows,cols,3

def compress(origin_16,output_8):

    array_data,geo,proj,rows,cols,couts= read_tiff(origin_16)

    compress_data = np.zeros((couts,rows,cols))

    for i in range(couts):
        band_max = np.max(array_data[i,:,:])
        band_min = np.min(array_data[i,:,:])

        cutmin,cutmax=cumulativehistogram(array_data[i,:,:],rows,cols,band_min,band_max)

        compress_scale = (cutmax-cutmin)/255
        
        for j in range(rows):
            for k in range(cols):
                if(array_data[i,j,k]<cutmin):
                    array_data[i,j,k]=cutmin

                if(array_data[i,j,k]>cutmax):
                    array_data[i,j,k]=cutmax

                compress_data[i,j,k] = (array_data[i,j,k]-cutmin)/compress_scale

    write_tiff(output_8,compress_data,rows,cols,couts,geo,proj)

def write_tiff(output_file,array_data,rows,cols,counts,geo,proj):

    Driver = gdal.GetDriverByName("Gtiff")
    dataset = Driver.Create(output_file,cols,rows,counts,gdal.GDT_Byte)

    dataset.SetGeoTransform(geo)
    dataset.SetProjection(proj)

    for i in range(counts):
        band = dataset.GetRasterBand(i+1)
        band.WriteArray(array_data[i,:,:])


def cumulativehistogram(array_data,rows,cols,band_min,band_max):
    """
    累计直方图统计
    """

    # 逐波段统计最值

    gray_level = int(band_max-band_min+1)
    gray_array = np.zeros(gray_level)

    counts=0
    for row in range(rows):
        for col in range(cols):
            gray_array[int(array_data[row,col]-band_min)]+=1
            counts+=1

    count_percent2 = counts*0.02
    count_percent98 = counts*0.98

    cutmax=0
    cutmin=0

    for i in range(1,gray_level):
        gray_array[i]+=gray_array[i-1]
        if(gray_array[i]>=count_percent2 and gray_array[i-1]<=count_percent2):
            cutmin = i+band_min

        if(gray_array[i]>=count_percent98 and gray_array[i-1]<=count_percent98):
            cutmax = i+band_min

    return cutmin,cutmax

if __name__ == '__main__':

    origin_16=r"D:\ZY3_MUX_E133.3_N47.7_20160722_L1A0003484148\ZY3_MUX_E133.3_N47.7_20160722_L1A0003484148.tiff"
    output_8 = r"D:\new22.tif"
    compress(origin_16,output_8)

结果

原始影像

本文算法压缩结果

gdal_translate工具压缩结果