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

doi:10.11947/j.AGCS.2018.20170690

Abstract

Abstract: 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.

Key words: high resolution remote sensing image, cloud detection, adaptive spectral threshold for cloud detection, LBP texture, superpixels

CLC Number:

P237

DONG Zhipeng, WANG Mi, LI Deren, WANG Yanli, ZHANG Zhiqi. A Cloud Detection Method for High Resolution Remote Sensing Imagery Based on the Spectrum and Texture of Objects[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(7): 996-1006.

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