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Spectral–Spatial Hyperspectral Image Classification Based on KNN

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Abstract

Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral–spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods.

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Acknowledgments

The authors would like to thank Dr. J. Li for providing the software of the LBP and LMLL methods. This paper was supported in part by the National Natural Science Foundation for Distinguished Young Scholars of China under Grant No. 61325007, the National Natural Science Foundation of China under Grant No. 61172161.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Kunshan Huang, Shutao Li, Xudong Kang & Leyuan Fang

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  1. Kunshan Huang
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  2. Shutao Li
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  3. Xudong Kang
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  4. Leyuan Fang
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Correspondence to Shutao Li.

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This article is part of the Topical Collection on Hyperspectral Imaging and Image Processing.

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Huang, K., Li, S., Kang, X. et al. Spectral–Spatial Hyperspectral Image Classification Based on KNN. Sens Imaging 17, 1 (2016). https://doi.org/10.1007/s11220-015-0126-z

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  • DOI: https://doi.org/10.1007/s11220-015-0126-z

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