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A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification

by K.Kavitha, S.Arivazhagan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 15
Year of Publication: 2010
Authors: K.Kavitha, S.Arivazhagan
10.5120/327-496

K.Kavitha, S.Arivazhagan . A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification. International Journal of Computer Applications. 1, 15 ( February 2010), 25-31. DOI=10.5120/327-496

@article{ 10.5120/327-496,
author = { K.Kavitha, S.Arivazhagan },
title = { A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 15 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number15/327-496/ },
doi = { 10.5120/327-496 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:22.606591+05:30
%A K.Kavitha
%A S.Arivazhagan
%T A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 15
%P 25-31
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A spatial classification technique incorporating a novel feature derivation method is proposed for classifying the heterogeneous classes present in the hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes present in the hyper spectral image are having different textures, textural classification is entertained. Wavelet based textural features extraction is entailed. Hyper spectral images are having dozen numbers of bands. Few mutually distinct bands are selected and wavelet transform is applied. For all the sub bands Gray Level Co-occurrence Matrix (GLCM) are calculated. From GLCMs co-occurrence features are derived for individual pixels. Apart from Co-occurrence features, statistical features are also calculated. Addition of statistical and co-occurrence features of individual pixels at individual bands form New Features for that pixel. By the process of adding these New Features of approximation band and individual sub-bands at the pixel level, Combined Features are derived. These Combined Features are used for classification. Support Vector Machines with Binary Hierarchical Tree (BHT) is developed to classify the data by One Against All (OAA) methodology. Airborne Visible Infra Red Imaging Sensor (AVIRIS) image of Cuprite -Nevada field is inducted for the experiment.

References
  1. David Landgrebe, "Some Fundamentals and Methods for Hyper spectral Image data Analysis", SPIE Photonics , pp.1-10,Jan-1999
  2. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Dordrecht, The Netherlands: Kluwer, 2003.
  3. G. F. Hughes, "On the mean accuracy of statistical pattern recognizers, "IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
  4. Yuliya Tarabalka, Jón Atli Benediktsson, Jocelyn Chanussot, "Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques", IEEE Trans. Geosci. Remote Sensing, vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
  5. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York: Wiley, 2003.
  6. P. K. Goel, S. O. Prasher, R. M. Patel, J. A. Landry, R. B. Bonnell, and A. A. Viau, "Classification of Hyperspectral Data by Decision trees and Artificial Neural Networks to identify weed stress and nitrogen status of corn," Comput. Electron. Agric., vol. 39, no. 2, pp. 67-93, 2003.
  7. H. Zhou, Z. Mao, and D. Wang, "Classification of coastal areas by airborne Hyperspectral image," in Proc. SPIE Opt. Technol. Atmos., Ocean, Environ. Stud., May 2005, vol. 5832, pp. 471-476.
  8. S. Subramanian, N. Gat, M. Sheffield, J. Barhen, and N. Toomarian, "Methodology for Hyperspectral Image Classification using Novel Neural Network," in Proc. SPIE Algorithms Multispectral Hyperspectral Imagery III, A. E. Iverson, S. S. Shen, Eds., Aug. 1997, vol. 3071, pp. 128-137.
  9. H. Yang, F. V. D. Meer, W. Bakker, and Z. J. Tan, "A Back-Propagation Neural Network for mineralogical mapping from AVIRIS data," Int. J.Remote Sens., vol. 20, no. 1, pp. 97-110, 1999.
  10. Hernández-Espinosa, M. Fernández-Redondo, and J. Torres-Sospedra, Some experiments with ensembles of neural networks for classification of hyperspectral images," in Proc. ISNN, vol. 1, 2004, pp. 912-917.
  11. Vaiphasa, "Innovative genetic algorithm for hyperspectral image classification,"in Proc. Int. Conf. Map Asia, 2003, p. 20.
  12. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for Hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43,no. 6, pp. 1351-1362, Jun. 2005.
  13. M. Fauvel, "Spectral and spatial methods for the classification of urban remote sensing data," Ph.D. dissertation, Grenoble Inst. Technol., Grenoble, France, 2007.
  14. A. Gualtieri and R. F. Cromp, "Support Vector Machines for Hyperspectral remote sensing classification," Proc. SPIE, vol. 3584, pp. 221-232,Jan. 1998.
  15. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Evaluation of Kernels for Multiclass Classification of Hyperspectral remote sensing data," in Proc. ICASSP, May 2006, pp. II-813-II-816.
  16. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Franc´es, and J. Calpe-Maravilla, "Composite Kernels for Hyperspectral Image Classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
  17. G. Mercier and F. Girard-Ardhuin, "Partially supervised oil-slick detection by SAR imagery using kernel expansion," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2839-2846, Oct. 2006.
  18. Mathieu Fauvel , Jocelyn Chanussot , Jon Atli Benediktsson, "Adaptive Pixel Neighborhood Definition for the Classification of Hyperspectral images with Support Vector Machines and Composite Kernel, in Proc. ICASSP, May 2006, pp. II-813-II-816.
  19. C. Lee and D. A. Landgrebe, "Feature Extraction based on Decision Boundaries, "IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 4, pp. 388-400, Apr. 1993.
  20. L. O. Jimenez and D. A. Landgrebe, "Hyperspectral data analysis and Supervised Feature Reduction via Projection Pursuit," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp. 2653-2667, Nov. 1999.
  21. Bor-Chen Kuo, David A. Landgrebe, "Nonparametric Weighted Feature Extraction for Classification", IEEE Trans. Geosci. Remote Sens., Vol. 42, No. 5, pp.1096-1105, May 2004
  22. R. M. Haralick, K. Shanmugam, I. Dinstein, "Texture Features for image classification." IEEE Trans. System Man Cybernetics, Vol.8, No.6, pp. 610-621,1973
  23. S. Arivazhagan, L. Ganesan,S.DeivaLakshmi, "Texture Classification using Wavelet Statistical Features", Journal of the Institution of Engineers(India),Vol.85, pp.51-55, 2003
  24. S. Arivazhagan, L. Ganesan, "Texture Classification using Wavelet Transform", Pattern Recognition Letters, Vol.24, pp. 1513-1521, 2003.
  25. P.S.Hiremath, S.Shivashankar, "Wavelet Based Features For Texture Classification", GVIP Journal, Vol. 6, No. 3,pp.55-58, December, 2006.
  26. 16. P.S.Hiremath, S. Shivashankar, and Jagadeesh Pujari," Wavelet Based Features For Color Texture Classification With Application To CBIR", International Journal of Computer Science and Network Security, Vol.6 No.9A,pp.124-132, September 2006.
  27. Rick Archibald and George Fann ," Feature Selection and Classification of Hyper spectral Images With Support Vector Machine", IEEE Trans. Geosci. Remote Sensing Letters, Vol.4, No.4, pp.674-678, October-2007.
  28. David Landgrebe, "Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data", pp.1-30, November 1998
  29. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
  30. Farid Melgani, Lorenzo Bruzzone, "Classification of Hyperspectral Remote Sensing Images With Support Vector Machines", IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 8, pp. 1778-1790, August 2004.
  31. Gustavo Camps-Valls, Joan Vila, "Composite Kernels for Hyper Spectral Image Classification", IEEE Trans. Geosci. Remote Sens. Letters, vol. 3, no. 1, pp. 93-97 January 2006
  32. M. Murat Dundar and A. Landgrebe, "A Cost-effective Semisupervised Classifier Approach with Kernels," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 1, pp. 264-270, Jan. 2004.
  33. Mingmin Chi, "Semi Supervised Classification of Hyper-spectral Images by SVMs Optimized in the Primal", IEEE Trans. Geosci. Remote Sensing, Vol. 45, no. 6, pp. 1870-1880, June 2007.
  34. Gustavo Camps-Valls, Tatyana V. Bandos Marsheva, Dengyong Zhou, "Semi-Supervised Graph-Based Hyper-spectral Image Classification", IEEE Trans. Geosci. Remote Sensing, Vol. 45, no. 10, pp. 3044-3054, October 2007.
  35. Gustavo Camps-Valls, Lorenzo Bruzzone, "Kernel-Based Methods for Hyperspectral Image Classification", IEEE Trans. Geosci. Remote Sensing, Vol.43, No.6; pp:1351-1363, June 2005
  36. Yakoub Bazi, Farid Melgani, "Toward an Optimal SVM Classification System for Hyper-spectral Remote Sensing Images", IEEE Trans. Geoscience. Remote Sensing, Vol.44, No.11; pp:3374-3376, November 2006
  37. Asbjørn Berge, Anne H. Schistad Solberg, "Structured Gaussian Components for Hyper spectral Image Classification", IEEE Trans. Geosci. Remote Sensing, Vol.44, No.11; pp:3386-3396, November 2006.
  38. Brian J. Ross and Anthony G. Gualtieri, Frank Fueten, Paul Budkewitsch, " Hyper spectral Image Analysis Using Genetic Programming", Genetic and Evolutionary Computation Conference, pp: 1196 - 1203, 2002.
  39. Baofeng Guo, Steve R. Gunn, R. I. Damper, James D.B. Nelson ,"Customizing Kernel Functions for SVM-Based Hyper spectral Image Classification", IEEE Image Processing, Vol. 47, no. 4, pp. 622-629, Apr-2008.
  40. David Landgrebe, "Information Extraction Principles and Methods for Multispectral and Hyper spectral Image Data", pp.1-30,November1998.DOI: www:cobweb.ecn.purdue.edu/~landgreb/GeoComp99.pdf
Index Terms

Computer Science
Information Sciences

Keywords

Multi-class Wavelet Co-occurrence features Feature Extraction Feature derivation Support Vector Machines

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