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Dimensionality Reduced Recursive Filter Features for Hyperspectral Image Classification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

Dimensionality reduction techniques have been immensely used in hyperspectral image classification tasks and is still a topic of great interest. Feature extraction based on image fusion and recursive filtering (IFRF) is a recent work which provides a framework for classification and produces good classification accuracy. In this paper, we propose an alternative approach to this technique by employing an efficient preprocessing technique based on average interband blockwise correlation coefficient followed by a stage of dimensionality reduction. The final stages involve recursive filtering and support vector machine (SVM) classifier. Our method highlights the utilization of an automated procedure for the removal of noisy and water absorption bands. Results obtained using experimentation of the proposed method on Aviris Indian Pines database indicate that a very low number of feature dimensions provide overall accuracy around 98 %. Four different dimensionality reduction techniques (LDA, PCA, SVD, wavelet) have been employed and notable results have been obtained, especially in the case of SVD (OA = 98.81) and wavelet-based approaches (OA = 98.87).

Keywords

Preprocessing Feature extraction Band selection Recursive filtering Wavelet IFRF SVM PCA LDA SVD 

References

  1. 1.
    Dash, M., Liu, H.: Dimensionality Reduction: Wiley Encyclopedia of Computer Science and Engineering. Wiley (2008)Google Scholar
  2. 2.
    Bharath Bhushan, D., Nidamanuri, R.R.: Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system. Adv. Space Res. 53(12), 1720–1734 (2014)CrossRefGoogle Scholar
  3. 3.
    Kang, X., Li, S., Benediktsson, J.A.: Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sens. 52(5), 2666–2677 (2014)CrossRefGoogle Scholar
  4. 4.
    Kang, X., Li, S., Benediktsson, J.A.: Feature extraction of hyperspectral images with image fusion and recursive filtering. IEEE Trans. Geosci. Remote Sens. 52(6), 3742–3752 (2014)CrossRefGoogle Scholar
  5. 5.
    Bharath Bhushan, D., Sowmya, V., Sabarimalai Manikandan, M., Soman, K.P.: An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery. In: Ocean Electronics (SYMPOL), 2011 International Symposium, IEEE, pp. 34–39 (2011)Google Scholar
  6. 6.
    John, S.-T., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press (2004)Google Scholar
  7. 7.
    Bo, L., Wang, L., Jiao, L.: Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation. Neural Comput. 18(4), 961–978 (2006)MATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Velez-Reyes, M., Jimenez, L.O.: Subset selection analysis for the reduction of hyperspectral imagery. IEEE Int. Geosci. Remote Sens. Symp. Proc. 3, (1998)Google Scholar
  9. 9.
    Bruce, L.M., Koger, C.H., Li, J.: Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Trans. Geosci. Remote Sens. 40(10), 2331–2338 (2002)Google Scholar
  10. 10.
    Kaewpijit, S., Le Moigne, J., El-Ghazawi, T.: Automatic reduction of hyperspectral imagery using wavelet spectral analysis. IEEE Trans. Geosci. Remote Sens. 41(4), 863–871 (2002)CrossRefGoogle Scholar
  11. 11.
    Burnase, S.R., Swamy, S.: Hyperspectral image reduction using discrete wavelet transform. IOSR J. Electr. Electron. Eng. 13–16 (2014)Google Scholar
  12. 12.
    Soman, K.P., Ramachandran, K.I.: Insight Into Wavelets From Theory to Practice. Prentice-Hall, New Delhi (2005)Google Scholar
  13. 13.
    Vaidyanathan, Parishwad, P.: Multirate Systems and Filter Banks. Pearson Education, India (1993)Google Scholar
  14. 14.
    Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graphics (TOG) 30(4), (2011). ACMGoogle Scholar
  15. 15.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACMTrans. Intell. Syst. Technol. 2(3), 27–127 (2011)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Centre for Excellance in Computational Engineering and Networking (CEN)CoimbatoreIndia

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