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Open AccessArticle

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

1
Dept. of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1QE, UK
2
School of Electronical and Power Engineering, Taiyuan University of Technology, Taiyuan 030000, China
3
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 536; https://doi.org/10.3390/rs11050536
Received: 25 January 2019 / Revised: 22 February 2019 / Accepted: 27 February 2019 / Published: 5 March 2019
(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)
To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology. View Full-Text
Keywords: hyperspectral image; image classification; superpixel; sparse representation; metric learning hyperspectral image; image classification; superpixel; sparse representation; metric learning
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MDPI and ACS Style

Sun, H.; Ren, J.; Zhao, H.; Yan, Y.; Zabalza, J.; Marshall, S. Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images. Remote Sens. 2019, 11, 536. https://doi.org/10.3390/rs11050536

AMA Style

Sun H, Ren J, Zhao H, Yan Y, Zabalza J, Marshall S. Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images. Remote Sensing. 2019; 11(5):536. https://doi.org/10.3390/rs11050536

Chicago/Turabian Style

Sun, He; Ren, Jinchang; Zhao, Huimin; Yan, Yijun; Zabalza, Jaime; Marshall, Stephen. 2019. "Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images" Remote Sens. 11, no. 5: 536. https://doi.org/10.3390/rs11050536

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