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Hyperspectral Image Classification Based on Segmented Local Binary Patterns

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Abstract

Recently, local binary patterns (LBP) coupled with principal component analysis has been developed for feature extraction of hyperspectral imagery, which has shown success over traditional methods but is limited in physical meaning representation due to the noise bands existing in hyperspectral data. In order to preserve the intrinsic geometrical structure of original data, we propose a segmented LBP (SLBP) to group correlative bands and then extract spatial-spectral features from each band group. The proposed approach employs the LBP operator on independent subspaces to characterize local texture information and distinct spectral signatures, along with a decision fusion system further improving discriminant power. The proposed approach is compared with several traditional and state-of-the-art methods on two benchmark datasets (i.e., the Indian Pines dataset and the Salinas Valley dataset). Experimental results demonstrate that the proposed SLBP strategy can yield superior classification performance (96.8% for the Indian Pines dataset with an improvement of approximately 6.4% and 4.2% when compared with LBP and MELBP, respectively; 98.1% for the Salinas Valley dataset with an improvement of approximately 3.5% and 1.3% compared with LBP and MELBP, respectively).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (41601344, 61803042) and Chongqing Research Program of Basic research and Frontier Technology (cstc2016jcyjA0539). The authors would also like to thank the editor and the anonymous reviewers for their insightful comments and suggestions in improving the quality of this paper.

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Authors and Affiliations

  1. School of Electronics and Control Engineering, Chang’an University, Xi’an, China

    Zhen Ye, Rui Dong & Lin Bai

  2. Xi’an Institute of Modern Control Technology, Xi’an, China

    Chengxuan Jin

  3. College of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China

    Yongjian Nian

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  1. Zhen Ye
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  2. Rui Dong
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  3. Lin Bai
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  4. Chengxuan Jin
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  5. Yongjian Nian
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Correspondence to Zhen Ye.

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Ye, Z., Dong, R., Bai, L. et al. Hyperspectral Image Classification Based on Segmented Local Binary Patterns. Sens Imaging 21, 15 (2020). https://doi.org/10.1007/s11220-020-0274-7

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  • DOI: https://doi.org/10.1007/s11220-020-0274-7

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