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Cloud Detection in ZY-3 Multi-Angle Remote Sensing Images

Cloud pollution on remote sensing images seriously affects the actual use rate of remote sensing images. Therefore, cloud detection of remote sensing images is an indispensable part of image preprocessing and image availability screening. Aiming at the lack of short wave infrared and
 thermal infrared bands in ZY-3 high-resolution satellite images resulting in the poor detection effect, considering the obvious difference in geographic height between cloud and ground surface objects, this paper proposes a thick and thin cloud detection method combining spectral information
 and digital height model (DHM) based on multi-scale features-convolutional neural network (MF-CNN) model. To verify the importance of DHM height information in cloud detection of ZY-3 multi-angle remote sensing images, this paper implements cloud detection comparison of the data set with and
 without DHM height information based on the MF-CNN model. The experimental results show that the ZY-3 multi-angle image with DHM height information can effectively improve the confusion between highlighted surface and thin cloud, which also means the assistance of DHM height information can
 make up for the disadvantage of high-resolution image lacking short wave infrared and thermal infrared bands.