Destriping hyperspectral imagery via spectral–spatial low-rank representation

Abstract

In this paper, we address the problem of removing striping noise in hyperspectral images (HSI). To this end, we develop a novel destriping method by taking advantage of the spectral and spatial information of the hyperspectral image simultaneously. To obtain satisfactory destriped results, our consideration is two-fold: (1) To model the spectral information of HSI, we utilize low-rank representation to exploit the low-rank property of HSI; (2) To incorporate the spatial information into our method, we adopt the Huber-Markov random field (HMRF) prior model to preserve the edge information of HSI while reducing the striping noise. Finally, we integrate the spectral and spatial model into a unified objective function. In addition, we also devise an effective optimization algorithm to minimize the objective function. The experimental results on real-world HSI data validate the efficacy of the proposed scheme for destriping of HSI.