A New Sparse Collaborative Low-Rank Prior Knowledge Representation for Thick Cloud Removal in Remote Sensing Images

Abstract

Efficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered for the targeted tasks of cloud detection and removal. This function is adept at capturing cloud characteristics across three dimensions, leveraging the sparsity of mode-1, -2, and -3 fibers simultaneously to achieve precise cloud detection. By incorporating the concept of tensor multi-rank, which describes the global correlation, we have developed a tri-fiber-wise sparse-based model that excels in both detecting and eliminating clouds from images. Furthermore, to ensure that the cloud-free information accurately matches the corresponding areas in the observed data, we have enhanced our model with an extended box-constraint strategy. The experiments showcase the notable success of the proposed method in cloud removal. This highlights its potential and utility in enhancing the accuracy of remote sensing imagery.