Study On Removing Cloud From Remote Sensing Image Based On Sparse Representation

Remote sensing technology plays an increasingly important role in the modernization ofnational defense, economic construction and other aspects as it can fast, timely, convenientlyprovides a variety of earth observation, tracking, positioning and navigation and other relatedservices. However, due to the presence of cloud, remote sensing image can not play its duevalue in the practical application. Still, since the presence of clouds obscured the objectinformation that we want to use on the surface of the earth, we can not use remote sensingimages to do further research work. Moreover, because of the changeable climate, it is verydifficult to get access to cloudless remote sensing images. Therefore, removing cloud ofremote sensing image is an urgent problem to be solved, and how to find an effective methodto remove the cloud, is a very important research problem in the field of remote sensingimage processing.This paper makes an in-dept study of the problem of removing cloud from remotesensing image, researching on cloud model in remote sensing image, remote sensing imageimaging theory, and the image sparse representation, morphological component analysistheory and method. On the basis of all of these, the author proposed a method to removecloud from remote sensing image based on the sparse representation, and the simulationresults verify the good effect of removing cloud from remote sensing image.First of all, according to the imaging theory of remote sensing image, this paper make adetailed analysis of the commonly used methods of removing cloud from remote sensingimage. Begin with the cloud model in remote sensing image, the paper analyzes the imagingmodel of the cloud and the degradation and restoration model of optical image. On the basisof these, this paper gives some common methods of removing cloud from remote sensingimage methods and their respective advantages and disadvantages, including thehomomorphism filtering method, wavelet transform, and data fusion method.Secondly, this paper elaborates the basic principles of sparse representation method andmorphological component analysis. Based on the in-dept research on sparse representationalgorithm, several solving methods for optimization problems in sparse representation areintroduced and summarized. Combined with the characteristics of sparse representation theory,a comprehensive introduction to the model assumptions and theoretical basis ofmorphological component analysis was given in this paper.Again, a detailed method of removing cloud from remote sensing image based on sparse representation is given in this paper. Combining Image sparse decomposition based onmorphological component concept with some sparse representation dictionaries in imagemorphological component analysis, this paper elaborates a kind of method to remove cloudfrom remote sensing image based on sparse representation, and gives the detailed steps andprocess.Finally, this paper shows the experimental results of the proposed method in the Matlabenvironment. Through the analysis of the experimental results, we verify the effectiveness ofthe proposed method in removing thick clouds in remote sensing images.