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Publication Type : Journal Article
Publisher : International Journal of Control Theory and Applications.
Source : International Journal of Control Theory and Applications, Volume 9, Number 10, p.4445-4451 (2016)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2016
Abstract : Hyperspectral images contain large spectral and spatial information's and hence it is widely used in the field of remote sensing for various application such as urban planning, disaster management and land use land cover classification. However, these images are usually corrupted by various kind of noises and which adversely affect the quality of images. In order to resolve thisissue, various preprocessing technique are exploited while dealing with hyperspectral images. convexdenoising using non-convex tight frame regularization technique is proposed as a preprocessing technique. After preprocessing, the images are classified using Orthogonal Matching Pursuit (OMP) algorithm. The classification results are evaluated interms of accuracy assessment measures. Also, the impact of the proposed preprocessing stageis compared with classification results of existing denoising techniques such as Total Variation(TV)denoising and wavelet based denoising. © International Science Press.
Cite this Research Publication : K. R. Rithu Vadhana, Dr. Neethu Mohan, and Dr. Soman K. P., “Convex denoising of hyperspectral images using non-convex tight frame regularization for improved sparsity based classification”, International Journal of Control Theory and Applications, vol. 9, pp. 4445-4451, 2016.