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

  • Wenning WangEmail author
  • Xuanqin Mou
  • Xuebin Liu
Original Paper


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.


Eigenvector spectra Feature extraction Limited training sample classification Hyperspectral image 



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


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Spectral Imaging TechnologyXi’an Institute of Optics and Precision Mechanics, CASXi’anChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.School of Information Science and EngineeringShandong Agricultural UniversityTai’anChina

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