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Open AccessArticle

Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification

by Haoyang Yu 1,2, Lianru Gao 1,*, Wenzhi Liao 3 and Bing Zhang 1,2
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Telecommunications and Information Processing, IMEC-TELIN-Ghent University, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1695; https://doi.org/10.3390/s18061695
Received: 26 April 2018 / Revised: 13 May 2018 / Accepted: 16 May 2018 / Published: 24 May 2018
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial coherence tasks. However, these methods are biased towards the direct use of spatial information as a whole, while discriminative spectral information is not well exploited. In this paper, we propose a novel method to couple both nonlocal spatial and local spectral similarity together in a single framework. In particular, the proposed approach exploits nonlocal spatial similarities by searching non-overlapped patches, whereas spectral similarity is analyzed locally within the locally discovered patches. By fusion of nonlocal and local information, we then apply group sparse representation (GSR) for classification based on a group structured prior. Experimental results on three real hyperspectral data sets demonstrate the efficiency of the proposed approach, and the improvements are significant over the methods that consider either nonlocal or local similarity. View Full-Text
Keywords: hyperspectral imagery classification; group sparse representation (GSR); nonlocal spatial similarity; local spectral similarity hyperspectral imagery classification; group sparse representation (GSR); nonlocal spatial similarity; local spectral similarity
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Yu, H.; Gao, L.; Liao, W.; Zhang, B. Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification. Sensors 2018, 18, 1695.

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