Matting-based automatic and accurate cloud detection for multisource satellite images

Abstract

The matting technique can accurately detect the differences between the foreground and the background of images, but most of the existing matting algorithms need foreground and background labels in advance, which leads to obvious limitations in the processing of mass images. With matting technology introduced into cloud detection for multisource satellite images, an automatic and accurate cloud detection algorithm is proposed. First, the S-curve is used for the original image to enhance the contrast between the cloud and the background, then the gray visible image is binarized by the ensemble threshold methods and a preliminary binarized map is obtained by the voting strategy. On this basis, the connected regions are calculated and the centers of the connected regions are taken as the seed point, then the trimap based on the connected regions is automatically generated. Finally, the improved Laplacian matrix and the conjugate gradient algorithm are used to solve the alpha value to obtain accurate results of cloud detection. The proposed method is compared with learning-based and robust matting-based cloud detection algorithms. Objective and subjective experiments based on images from the Fengyun-2G meteorological satellite, Landsat-8, and Sentinel-2 show that this proposed method can detect multiple types of clouds better and exhibits higher accuracy.