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Remote Sens. 2017, 9(9), 872; doi:10.3390/rs9090872

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

School of Computer Science, China University of Geosciences, Lumo Road 388, Wuhan 430074, China
Department of Geodesy and Geomatics Engineering, University of New Brunswick, 15 Dineen Drive, Fredericton, NB E3B 5A3, Canada
Author to whom correspondence should be addressed.
Received: 13 July 2017 / Revised: 14 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification. The recently published Multiscale Adaptive Sparse Representation (MASR) classifier has shown good performance in exploiting spatial information for HSI classification. But the spatial information is exploited by multiscale patches with fixed sizes of square windows. The patch can include all nearest neighbor pixels but these neighbor pixels may contain some noise pixels. Then another research proposed a Multiscale Superpixel-Based Sparse Representation (MSSR) classifier. Shape-adaptive superpixels can provide more accurate representation than patches. But it is difficult to select scales for superpixels. Therefore, inspired by the merits and demerits of multiscale patches and superpixels, we propose a novel algorithm called Multiscale Union Regions Adaptive Sparse Representation (MURASR). The union region, which is the overlap of patch and superpixel, can make full use of the advantages of both and overcome the weaknesses of each one. Experiments on several HSI datasets demonstrate that the proposed MURASR is superior to MASR and union region is better than the patch in the sparse representation. View Full-Text
Keywords: classification; hyperspectral image (HSI); multiscale union regions adaptive sparse representation (MURASR); multiscale spatial information classification; hyperspectral image (HSI); multiscale union regions adaptive sparse representation (MURASR); multiscale spatial information

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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Tong, F.; Tong, H.; Jiang, J.; Zhang, Y. Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification. Remote Sens. 2017, 9, 872.

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