Spectral-Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 1; pp. 373 - 385
• Main Authors: Ting Lu, Shutao Li, Leyuan Fang, Yi Ma, Benediktsson, Jon Atli
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Band spectra; Bands; Correlation; Estimation; Hyperspectral image (HSI) denoising; Hyperspectral imaging; Image restoration; Mathematical models; Noise measurement; Noise reduction; Pixels; Representations; sparse representation (SR); spatial similarity; Spectra; spectral correlation; Hyperspectral image (HSI) denoising; spectral correlation; sparse representation (SR); spatial similarity
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2015.2457614

In this paper, a novel spectral-spatial adaptive sparse representation (SSASR) method is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly correlated spectral information and highly similar spatial information via sparse representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band subsets. Each band subset contains multiple continuous bands with highly similar spectral characteristics. Then, within each band subset, shape-adaptive local regions consisting of spatially similar pixels are searched in spatial domain. This way, spectral-spatial similar pixels can be grouped. Finally, the highly correlated and similar spectral-spatial information in each group is effectively used via the joint sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated experiments. The numerical and visual comparison results demonstrate the effectiveness and superiority of the proposed method.