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Open AccessArticle

Improved Joint Sparse Models for Hyperspectral Image Classification Based on a Novel Neighbour Selection Strategy

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School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
2
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
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Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 905; https://doi.org/10.3390/rs10060905
Received: 1 May 2018 / Revised: 30 May 2018 / Accepted: 5 June 2018 / Published: 8 June 2018
(This article belongs to the Section Remote Sensing Image Processing)
Joint sparse representation has been widely used for hyperspectral image classification in recent years, however, the equal weight assigned to each neighbouring pixel is less realistic, especially for the edge areas, and one fixed scale is not appropriate for the entire image extent. To overcome these problems, we propose an adaptive local neighbour selection strategy suitable for hyperspectral image classification. We also introduce a multi-level joint sparse model based on the proposed adaptive local neighbour selection strategy. This method can generate multiple joint sparse matrices on different levels based on the selected parameters, and the multi-level joint sparse optimization can be performed efficiently by a simultaneous orthogonal matching pursuit algorithm. Tests on three benchmark datasets show that the proposed method is superior to the conventional sparsity representation methods and the popular support vector machines. View Full-Text
Keywords: hyperspectral images; classification; sparse representation; joint sparse model; adaptive local matrix hyperspectral images; classification; sparse representation; joint sparse model; adaptive local matrix
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MDPI and ACS Style

Gao, Q.; Lim, S.; Jia, X. Improved Joint Sparse Models for Hyperspectral Image Classification Based on a Novel Neighbour Selection Strategy. Remote Sens. 2018, 10, 905.

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