Abstract
Remote sensing images contaminated by clouds cannot be used for target recognition, image classification, and other applications, which leads to a lot of remote sensing data being wasted. We propose a low-rank and sparse constrained dark channel prior for cloud removal in remote sensing image sequence (LRSC-DCP). The sparse and low-rank constraints are used to find the position of thick clouds and remove thick clouds in cloud-contaminated images respectively. The remaining thin clouds can be eliminated by the dark channel prior. We compare the algorithm proposed in this article with the dark channel prior. Experimental results prove that the proposed method has better performance.
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