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Hyperspectral Imagery Classification Based on Sparse Feature and Neighborhood Homogeneity

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Abstract

In hyperspectral image classification, it is important to make use of the rich spectral information efficiently and to use the neighborhood information appropriately to alleviate the ‘salt and pepper noise pixel’. This paper presents a new hyperspectral image classification method based on Sparse Feature and Neighborhood Homogeneity (SF-NH). The core idea of SF-NH is to use sparse feature to express the hyperspectral image, and then the classification results preliminarily obtained by the Support Vector Machine (SVM) are revised by the neighborhood homogeneity. Experimental results on two classical hyperspectral data (i.e., Indian Pines, Saunas data) show that the proposed SF-NH method can greatly improve the classification accuracy.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant No. 61275010), Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20132304110007), and the Heilongjiang Natural Science Foundation (Grant No. F201409), and the Fundamental Research Funds for the Central Universities (Grant No. HEUCFD1410). We would like to thank Qunming Wang of the Hong Kong Polytechnic University for his careful proofreading, good suggestions and helpful discussions. The authors also would like to thank the handling editor and the reviewers for providing valuable comments.

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Correspondence to Jinghui Yang.

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Yang, J., Wang, L. & Qian, J. Hyperspectral Imagery Classification Based on Sparse Feature and Neighborhood Homogeneity. J Indian Soc Remote Sens 43, 445–457 (2015). https://doi.org/10.1007/s12524-014-0420-6

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  • DOI: https://doi.org/10.1007/s12524-014-0420-6

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