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Journal of Applied Remote Sensing

Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering
Author(s): Xiaoqing Tang; Junlong Chen; Yazhou Liu; Quan-sen Sun
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Paper 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.

Paper Details

Date Published: 22 December 2015
PDF: 14 pages
J. Appl. Rem. Sens. 9(1) 095977 doi: 10.1117/1.JRS.9.095977
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Xiaoqing Tang, Nanjing Univ. of Science and Technology (China)
Junlong Chen, Nanjing Univ. of Science and Technology (China)
Yazhou Liu, Nanjing Univ. of Science and Technology (China)
Quan-sen Sun, Nanjing Univ. of Science and Technology (China)

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