利用对象光谱与纹理实现高分辨率遥感影像云检测方法

A Cloud Detection Method for High Resolution Remote Sensing Imagery Based on the Spectrum and Texture of Objects

针对高分辨率遥感影像云检测过程中合适的云检测光谱阈值难以确定及影像中类云地物对云检测精度影响的问题,提出一种基于对象光谱与纹理的高分辨率遥感影像云检测方法。首先,对影像进行直方图均衡化处理,根据均衡化影像直方图获得合适的影像云检测光谱阈值。其次,用简单线性迭代聚类算法对影像进行分割生成分割对象,以对象为处理单元,根据云检测光谱阈值和对象光谱属性对对象进行云检测过滤,获得初始云检结果。然后,求得直方图均衡化影像的纹理图,根据对象的纹理均值及角二阶矩对初始云检测结果提纯,消除类云地物对云检测精度的影响。最后对提纯云区域进行区域增长及膨胀处理,获得最终的影像云检测结果。定性对比试验和定量评价结果表明,本文方法可以获得良好的影像云检测结果。

To solving the problems that the spectral threshold selection of image cloud detection and the influence of cloud-like ground objects on cloud detection results,a novel cloud detection method for HSRI based onthe spectrum and texture of objects is proposed.Firstly,histogram equalization is performed on the image,and then the appropriate image cloud detection spectral threshold is obtained according to the image equalization histogram.Secondly,the image is segmented to obtain superpixels using the simple linear iterative clustering algorithm.The cloud in the image is initially detected based on cloud detection threshold and spectral attributes of superpixels.Thirdly,the local binary patterns(LBP)texture image of histogram equalization image is obtained.The initial cloud detection image is refined based on the gray mean value and angular second moment of the superpixels LBP texture to eliminate the influence of cloud like objects.Finally,the cloud detection image is processed using region growing algorithm and expansion algorithm to obtain accurate cloud detection results.The experimental results show that the proposed method can obtain good cloud detection results.