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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.4623-4630 (2016)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2016
Abstract : Hyperspectral sensors capture images in hundreds of spectral bands spanning almost all the regions in the electromagnetic spectrum. These images consists of many noisy bands. Hence, a strong diffusion scheme (denoising) is required to extract the meaningful spatial information present in the noisy spectral bands. In this paper, one dimensional signal denoising based on weighted regularized least square (LS) method is mapped to two dimensional hyperspectral image (HSI) denoising and the superiority of this method is determined on the basis of the classification accuracies of the hyperspectral image obtained using Grand Unified Regularized Least Square (GURLS) library and LibSVM, the support vector machines library. The proposed method brings out the efficiency of LS denoising based on the classification accuracies obtained on classifying the denoised image. The obtained results are also compared with the accuracy obtained on classifying the original image without denoising. The analysis is also extended to the classification of the images denoised using the conventional denoising techniques such as Total Variation (TV) and LF (Legendre Fenchel) based denoising. From the analysis, it is observed that the classification accuracies of the images denoised using the proposed method is much higher than the conventional denoising methods. Hence, showing that the LS based denoising is an efficient method which provides a denoised output almost similar to the original image. © International Science Press.
Cite this Research Publication : P. Megha, Swarna, M., K. Dixon, D. Merlin, Dr. Soman K. P., and Sowmya, “Impact of least square denoising on kernel based hyperspectral image classification”, International Journal of Control Theory and Applications, vol. 9, pp. 4623-4630, 2016.