TOTAL VARIATION REGULARIZED LOW-RANK SPARSITY DECOMPOSITION FOR BLIND CLOUD AND CLOUD SHADOW REMOVAL FROM MULTITEMPORAL IMAGERY

This paper proposes a spatial-spectral total variation (TV) regularized low-rank sparsity decomposition model for blind cloud and cloud shadow (cloud/shadow) detection and removal of multitemporal remote sensing imagery. Our concept is to decompose the contaminated image into the surface-reflected component and the cloud/shadow component. Low-rank regularization is utilized to model the spectral-temporal correlation of the surface-reflected component, meanwhile, the l(1)-norm and spatial-spectral total variation regularization is employed to describe the sparse prior and spatial-spectral continuity of the cloud/shadow component. To better preserve the information in cloud/shadow-free areas, the cloud/shadow detection results obtained as a by-product of our method are used to guide the information compensation from the original contaminated images. Several experiments are presented to demonstrate the effectiveness of the proposed method.