使用numpy逐像素计算栅格的像素均值 [英] calculate pixel by pixel mean of the rasters using numpy

问题描述

由于两个栅格(raster1和raster2)彼此重叠，因此我想通过计算每个重叠像素的均值来制作新的栅格；即，生成的新栅格的计算公式为:

Since the two rasters (raster1 and raster2) overlap each other, I want to make new raster by calculating mean of each overlapped pixels; i.e., The resulting new raster is calculated as:

new = [[mean(1,3), mean(1,3), mean(1,3), mean(1,3), mean(1,3)],[mean(2,4),mean(2,4),mean(2,4),mean(2,4),mean(2,4)]]


import numpy as np    
raster1 = np.array([[1,1,1,1,1],[2,2,2,2,2]])
raster2 = np.array([[3,3,3,3,3],[4,4,4,4,4]])

new = np.mean(raster1,raster2,axis=1)
print (new.tolist())

怎么了?

推荐答案

也许我误解了你，但是你想要吗?

Maybe I misunderstood you but do you want?

raster = (raster1 + raster2) / 2

实际上，在这种情况下，您甚至不需要np.mean，只需使用矩阵运算即可.

Actually in this case you don't even need np.mean, just use matrix operations.

np.mean用于处理特定轴上单个矩阵的计算平均值，因此情况有所不同.

np.mean is used to deal with calculating mean for a single matrix on specific axis, so it is a different situation.

这篇关于使用numpy逐像素计算栅格的像素均值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！