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

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

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.

Keywords

Eigenvector spectra Feature extraction Limited training sample classification Hyperspectral image 

Notes

Acknowledgements

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

References

  1. 1.
    Hughes, G.F.: On the mean accuracy of statistical pattern recognition. IEEE Trans. Inf. Theory 14(1), 55–63 (1968)CrossRefGoogle Scholar
  2. 2.
    Pan, B., Shi, Z., Xu, X.: MugNet: deep learning for hyperspectral image classification using limited samples. ISPRS J. Photogramm. Remote Sens. 145, 108–119 (2018)CrossRefGoogle Scholar
  3. 3.
    Makantasis, K., et al.: Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: International geoscience and remote sensing symposium (IGARSS 2015)Google Scholar
  4. 4.
    Pan, B., Shi, Z., Xu, X.: Analysis for the weakly pareto optimum in multiobjective-based hyperspectral band selection. IEEE Trans Geosci. Remote Sens. 57, 3729–3740 (2019)CrossRefGoogle Scholar
  5. 5.
    Pan, B., et al.: CoinNet: copy initialization network for multispectral imagery semantic segmentation. IEEE Geosci. Remote Sens. Lett. 16, 816–820 (2019)CrossRefGoogle Scholar
  6. 6.
    Shahdoosti, H.R., Mirzapour, F.: Spectral–spatial feature extraction using orthogonal linear discriminant analysis for classification of hyperspectral data. Eur. J. Remote Sens. 50(1), 111–124 (2017)CrossRefGoogle Scholar
  7. 7.
    Kuo, B.C., Landgrebe, D.A.: Nonparametric weighted feature extraction for classification. IEEE Trans. Geosci. Remote Sens. 42(5), 1096–1105 (2004)CrossRefGoogle Scholar
  8. 8.
    Kuo, B., Chang, K.: Feature extractions for small sample size classification problem[J]. IEEE Trans. Geosci. Remote. Sens. 45(3), 756–764 (2007)CrossRefGoogle Scholar
  9. 9.
    Kuo, B.C., Li, C.H., Yang, J.M.: Kernel nonparametric weighted feature extraction for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 47(4), 1139–1155 (2009)CrossRefGoogle Scholar
  10. 10.
    Yang, J., Yu, P., Kuo, B.: A nonparametric feature extraction and Its application to nearest neighbor classification for hyperspectral image data. IEEE Trans. Geosci. Remote Sens. 48(3), 1279–1293 (2010)CrossRefGoogle Scholar
  11. 11.
    Huang, H., Kuo, B.: Double nearest proportion feature extraction for hyperspectral-image classification. IEEE Trans. Geosci. Remote Sens. 48(11), 4034–4046 (2010)Google Scholar
  12. 12.
    Jia, X., Kuo, B., Crawford, M.M.: Feature mining for hyperspectral image classification. Proc. IEEE 101(3), 676–697 (2013)CrossRefGoogle Scholar
  13. 13.
    Imani, M., Ghassemian, H.: Band clustering-based feature extraction for classification of hyperspectral images using limited training samples. IEEE Geosci. Remote Sens. Lett. 11(8), 1325–1329 (2014)CrossRefGoogle Scholar
  14. 14.
    Kamandar, M., Ghassemian, H.: Linear feature extraction for hyperspectral images based on information theoretic learning. IEEE Geosci. Remote Sens. Lett. 10(4), 702–706 (2013)CrossRefGoogle Scholar
  15. 15.
    Wen, J., Tian, Z., Liu, X.: Neighborhood preserving orthogonal PNMF feature extraction for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(2), 759–768 (2013)CrossRefGoogle Scholar
  16. 16.
    Sharma, A., Paliwal, K.K.: Linear discriminant analysis for the small sample size problem: an overview. Int. J. Mach. Learn. Cybern. 6(3), 443–454 (2014)CrossRefGoogle Scholar

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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