Advertisement

Hyperspectral Remote Sensing Images Feature Extraction Based on Weighted Classwise Non-locality Preserving Projection

  • Jing LiuEmail author
  • Ting-ting Li
  • Tong Zhang
  • Yi Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

In order to solve the high dimensionality and high spectral correlation problems of hyperspectral remote sensing images (HRSIs), a new feature extraction method, named weighted classwise non-locality preserving projection (WCNLPP), is proposed. WCNLPP introduces uncorrelation coefficient to express the dissimilarity degree between the samples of different classes and constructs a non-nearest neighbor graph, such that the non-locality manifold structure of the samples is preserved after feature extraction. Firstly, principal component analysis (PCA) is used to reduce dimensionality and remove the spectral correlation of HRSIs; then, WCNLPP is utilized to guide the procedure of feature extraction after PCA; finally, minimum distance (MD) classifier and discriminant analysis (DA) classifier are used to perform terrain classification in the final feature subspace. The experimental results based on two real HRSIs show that, comparing with PCA, linear discriminant analysis (LDA) and classwise non-locality preserving projection (CNLPP) methods, the presented WCNLPP method can improve the terrain recognition accuracy.

Keywords

Linear discriminant analysis (LDA) Non-locality preserving projection (NLPP) Feature extraction Hyperspectral remote sensing images (HRSIs) 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61672405), the Natural Science Foundation of Shaanxi Province of China (No. 2018JM4018), the Fundamental Research Funds for the Central Universities (No. JB170204).

References

  1. 1.
    Zhang, B.: Frontier of hyperspectral image processing and information extraction. J. Remote Sens. 20(5), 1062–1090 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhao, Y., Zhang, L.: Application of hyperspectral remote sensing. Urban Geogr. 4, 187 (2017)Google Scholar
  3. 3.
    Du, P., Xia, J., Xue, Z., et al.: Review of hyperspectral remote sensing image classification. J. Remote Sens. 20(2), 236–256 (2016)Google Scholar
  4. 4.
    Sharma, A., Paliwal, K.: Linear discriminant analysis for the small sample size problem: an overview. Int. J. Mach. Learn. Cybern. 6(3), 443–454 (2014)CrossRefGoogle Scholar
  5. 5.
    Abdi, H., Williams, L.: Principal component analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 433–459 (2010)CrossRefGoogle Scholar
  6. 6.
    Feng, L., Liu, Y., Liu, Y.: Manifold learning and algorithm analysis. Comput. Age 4, 1–4 (2017)Google Scholar
  7. 7.
    Lunga, D., Prasad, S., Crawford, M.: Manifold-learning-based feature extraction for classification of hyperspectral data: a review of advances in manifold learning. IEEE Sig. Process. Mag. 31(1), 55–66 (2014)CrossRefGoogle Scholar
  8. 8.
    Lzenman, A.: Introduction to manifold learning. Wiley Interdisc. Rev. Comput. Stat. 4(5), 439–446 (2012)CrossRefGoogle Scholar
  9. 9.
    Wang, B., Gao, X., Jie, L., et al.: A level set method with shape priors by using locality preserving projections. Neurocomputing 170, 188–200 (2015)CrossRefGoogle Scholar
  10. 10.
    Wang, W., Zhang, J.: Kernel based class-wise non-locality preserved projection. Pattern Recogn. Artif. Intell. 22(05), 769–773 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electronic EngineeringXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.School of Electronic EngineeringXidian UniversityXi’anChina

Personalised recommendations