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Modified eigenvector-based feature extraction for hyperspectral image classification using limited samples

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Abstract

Classical supervised feature extraction methods, such as linear discriminant analysis (LDA) and nonparametric weighted feature extraction (NWFE), and search for projection directions through which the ratio of a between-class scatter matrix to a within-class scatter matrix can be maximized. The two feature extraction methods can obtain good classification results when training samples are sufficient; however, the effect is nonideal when samples are insufficient. In this study, the eigenvector spectra of LDA and NWFE are modified using spectral distribution information, which is locally unstable under the condition of a few samples. Experiments demonstrate that the proposed method outperforms several conventional feature extraction methods.

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Acknowledgements

Funding was provided by the National Natural Science Foundation of China (Grant No. 61501456).

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Authors and Affiliations

  1. Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, CAS, Xi’an, China

    Wenning Wang & Xuebin Liu

  2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Wenning Wang & Xuanqin Mou

  3. School of Information Science and Engineering, Shandong Agricultural University, Tai’an, Shandong, China

    Wenning Wang

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  1. Wenning Wang
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  2. Xuanqin Mou
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  3. Xuebin Liu
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Correspondence to Wenning Wang.

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Wang, W., Mou, X. & Liu, X. Modified eigenvector-based feature extraction for hyperspectral image classification using limited samples. SIViP 14, 711–717 (2020). https://doi.org/10.1007/s11760-019-01604-3

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  • DOI: https://doi.org/10.1007/s11760-019-01604-3

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