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Hyperspectral image classification via compact-dictionary-based sparse representation

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Abstract

In this paper, a compact-dictionary-based sparse representation (CDSR) method is proposed for hyperspectral image (HSI) classification. The proposed dictionary in CDSR is dynamically generated according to the spatial and spectral context of each pixel. It can effectively shrink the decision range for classification, and reduce the computational burden since the compact dictionary is composed of the classes correlated with the target pixel in terms of spatial location and spectral information. In order to obtain better spatial context information, a spatial location expanding strategy is designed for spreading local explicit label information to a wider region. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with some widely used HSI classification approaches.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No.61401386 and 61802328. The authors would like to thank Prof. D. Landgrebe and Prof. P. Gamba for providing the hyperspectral data set. In addition, we also would like to thank the reviewers for their constructive comments on this manuscript.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China

    Chunhong Cao, Liu Deng, Wei Duan, Fen Xiao, WanChun Yang & Kai Hu

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  1. Chunhong Cao
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  2. Liu Deng
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  3. Wei Duan
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  4. Fen Xiao
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  5. WanChun Yang
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  6. Kai Hu
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Correspondence to Liu Deng.

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Cao, C., Deng, L., Duan, W. et al. Hyperspectral image classification via compact-dictionary-based sparse representation. Multimed Tools Appl 78, 15011–15031 (2019). https://doi.org/10.1007/s11042-018-6885-5

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