本实验的主要目的是基于遥感图像计算灰度共生矩阵，并基于该矩阵计算多种纹理特征。所有的计算结果已与ENVI结果进行对比，实验结果一致。
This folder mainly contains the code of extracting many kinds of GLCM features based on remote sensing image. The experimental results have been verified by the corresponding results of ENVI.
本实验的主要目的是基于遥感图像计算灰度共生矩阵，并基于改矩阵计算多种纹理特征。主要包括：均值、方差、同质性、对比度、异质性、熵、角二阶矩（能量）、相关性以及自相关性。
所有特征均与ENVI所得到的特征结果进行了验证，除相关性外，其余特征均与ENVI结果一致。相关性可能是因为所用的计算公式不一致，导致计算结果有出入。
VSCode
Python 3.7
后续代码讲解会上传至Bilili。
我的Bilili：https://space.bilibili.com/315476253
h, w = img.shape
ValueError: too many values to unpack (expected 2)
根据我看到的定义这些统计量按照公式似乎应该是单独的值，是因为省略了求和吗？
您好，请问这个代码有做优化吗？目前运行时，比较慢，而且太消耗内存了，如果速度能和ENVI一样就更好了
应该是bilibili吧，大佬手急打错了
How to extract numeric values?
同学你好，我这边的灰度图像的像素值范围大概在200~800，得到的效果图与ENVI得到的效果图的趋势差不多，但是数据差距大概在100倍，ENVI得到的数据范围在0-1
Hi,
Thanks for sharing the code. I found all subfigures became black (0) or white (255) when I ran 4 steps (2, 4, 8, 16) and 4 angles(0, 45, 90, 135), but it was okay when I only ran 1 step and 1 angle. does the code need to be changed a little bit?
Thanks!
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