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

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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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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Keywords

  • Eigenvector spectra
  • Feature extraction
  • Limited training sample classification
  • Hyperspectral image