In this paper, a new cloud removal method in remote sensing images is adopted.Based on the theory of compressive sensing,this method combines K-SVD dictionary learning with the orthogonal matching pursuit(OMP) algorithm of sparse representation.At the same time, a specific sorting rule is added to the process of dictionary atoms training, so that each image dictionary has its own image properties while its atoms also have a similar arrangement order to reduce the interference between image differences.In this method,the good effect of reconstruction of the contaminated region by clouds and shadows in remote sensing images is achieved.To illustrate the performance of the proposed method,experiments on two sets of data of multitemporal ZY-3 images at the same area are discussed.
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