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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dash, M., Liu, H.: Dimensionality Reduction: Wiley Encyclopedia of Computer Science and Engineering. Wiley (2008)
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)
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)
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)
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)
John, S.-T., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press (2004)
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)
Velez-Reyes, M., Jimenez, L.O.: Subset selection analysis for the reduction of hyperspectral imagery. IEEE Int. Geosci. Remote Sens. Symp. Proc. 3, (1998)
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)
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)
Burnase, S.R., Swamy, S.: Hyperspectral image reduction using discrete wavelet transform. IOSR J. Electr. Electron. Eng. 13–16 (2014)
Soman, K.P., Ramachandran, K.I.: Insight Into Wavelets From Theory to Practice. Prentice-Hall, New Delhi (2005)
Vaidyanathan, Parishwad, P.: Multirate Systems and Filter Banks. Pearson Education, India (1993)
Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graphics (TOG) 30(4), (2011). ACM
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACMTrans. Intell. Syst. Technol. 2(3), 27–127 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Lekshmi Kiran, S., Sowmya, V., Soman, K.P. (2016). Dimensionality Reduced Recursive Filter Features for Hyperspectral Image Classification. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 380. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2523-2_54
Download citation
DOI: https://doi.org/10.1007/978-81-322-2523-2_54
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2522-5
Online ISBN: 978-81-322-2523-2
eBook Packages: EngineeringEngineering (R0)