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Sensors 2016, 16(10), 1718; doi:10.3390/s16101718

Hierarchical Sparse Learning with Spectral-Spatial Information for Hyperspectral Imagery Denoising

1,2,* , 1,2
and
1,2
1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China
2
Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jonathan Li
Received: 20 July 2016 / Revised: 6 September 2016 / Accepted: 9 October 2016 / Published: 17 October 2016
(This article belongs to the Section Remote Sensors)

Abstract

During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches. All patches can be regarded as consisting of clean image component, Gaussian noise component and sparse noise component. The first term is depicted by a linear combination of dictionary elements, where Gaussian process with Gamma distribution is applied to impose spatial consistency on dictionary. The last two terms are utilized to fully depict the noise characteristics. Furthermore, the sparseness of the model is adaptively manifested through Beta-Bernoulli process. Calculated by Gibbs sampler, the proposed model can directly predict the noise and dictionary without priori information of the degraded HSI. The experimental results on both synthetic and real HSI demonstrate that the proposed approach can better suppress the existing various noises and preserve the structure/spectral-spatial information than the compared state-of-art approaches. View Full-Text
Keywords: hierarchical sparse learning; denoising; spectral-spatial information; hyperspectral images hierarchical sparse learning; denoising; spectral-spatial information; hyperspectral images
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, S.; Jiao, L.; Yang, S. Hierarchical Sparse Learning with Spectral-Spatial Information for Hyperspectral Imagery Denoising. Sensors 2016, 16, 1718.

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