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Publication Type : Conference Paper
Publisher : 6th International Conference on Advances in Computing and Communications , ICACC-2016
Source : 6th International Conference on Advances in Computing and Communications , ICACC-2016, Elsevier, Volume 93, Rajagiri School of Engineering & Technology, p.396-402 (2016)
Keywords : accuracy, Band selection, Classification (of information), Dimensionality reduction, GURLS, Hyper-spectral images, image classification, Independent component analysis, Kernel methods, LIBSVM, Singular value decomposition, Spectroscopy
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication
Year : 2016
Abstract : Hyperspectral images have abundant of information stored in the various spectral bands ranging from visible to infrared region in the electromagnetic spectrum. High data volume of these images have to be reduced, preserving the original information, to ensure efficient processing. In this paper, dimensionality reduction is done on Indian Pines and Salinas-A datasets using inter band block correlation coefficient technique followed by Singular Value Decomposition (SVD) and QR decomposition. The dimensionally reduced images are classified using GURLS and LibSVM. Classification accuracies of the original image is compared to that of the dimensionally reduced image. The experimental analysis shows that, for 10% training sample the overall accuracy, average accuracy and kappa coefficient of the dimensionally reduced image (about 50% of the dimension is reduced) is i)83.52%, 77.18%, 0.8110 for Indian Pines and ii)99.53%, 99.40%, 0.9941 for Salinas-A dataset which is comparable to that of original image i)84.67%, 82.28%, 0.8247 for Indian Pines and ii)99.32%, 99.18%, 0.9916 for Salinas-A dataset. © 2016 The Authors. Published by Elsevier B.V.
Cite this Research Publication : R. Reshma, Sowmya, Dr. Soman K. P., J., M., and J., J., “Dimensionality Reduction Using Band Selection Technique for Kernel Based Hyperspectral Image Classification”, in 6th International Conference on Advances in Computing and Communications , ICACC-2016, Rajagiri School of Engineering & Technology, 2016, vol. 93, pp. 396-402.