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Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising

Publication Type : Journal Article

Publisher : Indian Journal of Science and Technology

Source : Indian Journal of Science and Technology, Volume 9, Issue 33 (2016)

Url : http://www.indjst.org/index.php/indjst/article/view/99594

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Year : 2016

Abstract : 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.

Cite this Research Publication : Reshma R, Sowmya, and Dr. Soman K. P., “Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising”, Indian Journal of Science and Technology, vol. 9, no. 33, 2016.

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