基于因子组稀疏正则的多时相遥感图像去云方法OA

Cloudy weather causes cloud coverage and information loss in multi-temporal remote sensing image(MTRSI),affecting the subsequent application performance.In recent years,cloud removal methods based on low-rank matrix/tensor decomposition and sparse regularization have ignored the consistency features across different bands and different temporal dimensions of image.To this end,under the framework of low-rank matrix decomposition,a novel MTRSI cloud removal model based on factor group sparsity regularization was proposed.Specifically,global spatiotemporal correlations of clean image were characterized by the model using low-rank matrix decomposition;by imposing group sparsity regularization on the factors corresponding to multi-dimensional data,the consistency of spatially smooth regions across bands and time was accurately captured and constrained.Furthermore,a proximal alternating minimization algorithm embedded with the alternating direction method of multipliers was designed to decompose the original optimization problem into two sub-problems,namely low-rank constraint and sparse regularization term optimization,which were updated alternately and iteratively,thereby achieving an efficient solution to the original problem.In simulation experiments,compared with the suboptimal method,the average peak signal-to-noise ratio of the proposed cloud removal method increases by 18.01%;the average spectral angle mapper decreases by 43.01%;the average structural similarity increases by 4.1×10-3;the average correlation coefficient increases by 2.3×10-3;and the required running time decreases by 78.43%.Meanwhile,better cloud removal results are also achieved in real-scene experiments.