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

Sun, He; Ren, Jinchang; Zhao, Huimin; Yan, Yijun; Zabalza, Jaime; Marshall, Stephen

doi:10.3390/rs11050536

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

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Dept. of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1QE, UK

School of Electronical and Power Engineering, Taiyuan University of Technology, Taiyuan 030000, China

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)

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Abstract

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

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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.

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

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

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