Remote Sensing Applications and Decision Support

Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering

[+] Author Affiliations
Xiaoqing Tang, Junlong Chen, Yazhou Liu, Quansen Sun

Nanjing University of Science and Technology, School of Computer Science and Engineering, No. 200, Xiao Ling Wei, Nanjing 210094, China

J. Appl. Remote Sens. 9(1), 095977 (Dec 22, 2015). doi:10.1117/1.JRS.9.095977
History: Received June 2, 2015; Accepted November 19, 2015
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Abstract.  We present a hyperspectral image classification method based on sparse representation and superpixel segmentation. The presented method includes two main stages, which are sparse representation of extended multiattribute profiles (EMAPs) and superpixel segmentation of EMAPs. Specifically, we use the sparse representation of EMAPs to obtain the initial label of the pixel in the hyperspectral data. In addition, unsupervised superpixel segmentation is applied to EMAPs to generate the spatial constraint of the data. By refining the spectral classification results with the spatial constraints, the accuracy of classification is improved by a substantial margin. Our experiments reveal that the proposed approach yields state-of-the-art classification results for different hyperspectral datasets.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Xiaoqing Tang ; Junlong Chen ; Yazhou Liu and Quansen Sun
"Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering", J. Appl. Remote Sens. 9(1), 095977 (Dec 22, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095977


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