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Improvement in Kernel based Hyperspectral Image Classification Using Legendre Fenchel Denoising


  • Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore- 641112, Tamil Nadu, India


Hyperspectral images have bulk of information which are widely used in the field of remote sensing. One of the main problems faced by these images is noise. This emphasizes the importance of denoising techniques for enhancing the image quality. In this paper, Legendre Fenchel Transformation (LFT) is used for preprocessing the Indian Pines Dataset. LFT reduces the noise of each band of the hyperspectral image without affecting the edge information. Signal to noise ratio is computed which helps to evaluate the performance of denoising. Further, the denoised image is classified using GURLS and LibSVM and the various accuracies are estimated. The experimental analysis shows that the overall and classwise accuracies are more for the preprocessed data classification when compared to the classification without preprocessing. The classification accuracy is improved with denoising of hyperspectral image.


Classification, Denoising, GURLS, Hyperspectral Image, Kernel Methods, Legendre Fenchel, LibSVM

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  • Santhosh S, Abinaya N, Rashmi G, Sowmya V, Soman KP. A novel approach for denoising coloured remote sensing image using Legendre Fenchel Transformation. 2014 International Conference on Recent Trends in Information Technology (ICRTIT); 2014 Apr. p. 1–6.
  • Handa A. Applications of Legendre-Fenchel transformation to computer vision problems. Department of Computing at Imperial College London; 2011 Jul. p. 1–35.
  • Soman KP, Loganathan R, Ajay V. Machine learning with SVM and other Kernel methods. PHI Learning Pvt. Ltd; 2009 Feb.
  • Tacchetti A, Mallapragada PK, Santoro M, Rosasco L. Gurls: a least squares library for supervised learning. The Journal of Machine Learning Research. 2013 Oct; 14(1):3201–5.
  • Chang CC, Lin CJ. Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology. 2011 Apr; 2(3).
  • Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks. 2002 Mar; 13(2):415–25.
  • Rudin LI, Stanley O, Emad F. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena. 1992 Nov; 60(1–4):259–68.
  • Scholkopf B, Smola AJ. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press, Cambridge; 2001.
  • Gualtieri J, Chettri SR, Cromp R, Johnson L. Support vector machine classifiers as applied to aviris data. Proceeding Eighth JPL Airborne Geoscience Workshop, Citeseer; 1999 Feb.
  • Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques; 2007 Jun. p. 249–68.
  • Xu L, Fan L, Wong A, Clausi DA. Hyperspectral image denoising using a spatial–spectral monte carlo sampling approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015 Jun; 8(6):3025–38.
  • Aswathy C, Sowmya V, Soman KP. ADMM based hyperspectral image classification improved by denoising using Legendre Fenchel transformation. Indian Journal of Science and Technology. 2015 Sep; 8(24):1–9. DOI: 10.17485/ijst/2015/v8i24/80030.
  • Haridas N, Sowmya V, Soman KP. Gurls vs Libsvm: Performance comparison of kernel methods for hyperspectral image classification. Indian Journal of Science and Technology. 2015 Sep; 8(24):1–10. DOI: 10.17485/ijst/2015/v8i24/80843.


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