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

Xiaoqing Tang; Junlong Chen; Yazhou Liu; Quan-sen Sun

doi:10.1117/1.JRS.9.095977

22 December 2015 Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering

Xiaoqing Tang, Junlong Chen, Yazhou Liu, Quan-sen Sun

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095977 (December 2015). https://doi.org/10.1117/1.JRS.9.095977

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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Xiaoqing Tang, Junlong Chen, Yazhou Liu, and Quan-sen Sun "Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering," Journal of Applied Remote Sensing 9(1), 095977 (22 December 2015). https://doi.org/10.1117/1.JRS.9.095977

Published: 22 December 2015

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