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A discriminant sparse representation graph-based semi-supervised learning for hyperspectral image classification

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Abstract

The classification of hyperspectral image with a paucity of labeled samples is a challenging task. In this paper, we present a discriminant sparse representation (DSR) graph for semi-supervised learning (SSL) to address this problem. For graph-based methods, how to construct a graph among the pixels is the key to a successful classification. Our graph construction method contains two steps. Sparse representation (SR) method is first employed to estimate the probability matrix of the pairwise pixels belonging to the same class, and then this probability matrix is integrated into the SR graph, which can be obtained by solving an ℓ 1 optimization problem, to form a DSR graph. Experiments on Hyperion and AVIRIS hyperspectral data show that our proposed method outperforms state of the art.

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Acknowledgments

This work is supported by the Project of the National Natural Science Foundation of China No.61433007 and No.61401170.

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Authors and Affiliations

  1. Science and Technology on Multi-spectral Information Processing Laboratory, School of Automation Huazhong University of Science and Technology, Wuhan, 430074, China

    Yuanjie Shao, Changxin Gao & Nong Sang

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  1. Yuanjie Shao
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  2. Changxin Gao
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  3. Nong Sang
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Correspondence to Nong Sang.

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Shao, Y., Gao, C. & Sang, N. A discriminant sparse representation graph-based semi-supervised learning for hyperspectral image classification. Multimed Tools Appl 76, 10959–10971 (2017). https://doi.org/10.1007/s11042-016-3371-9

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  • DOI: https://doi.org/10.1007/s11042-016-3371-9

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