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Effect of AB filter denoising on ADMM based Hyperspectral Image Classification

Publication Type : Journal Article

Publisher : International Journal of Applied Engineering Research (IJAER)

Source : International Journal of Applied Engineering Research (IJAER), Volume 10, Issue 73, p.127-131 (2015)

Url : https://www.researchgate.net/publication/302421470_Effect_of_AB_filter_denoising_on_ADMM_based_Hyperspectral_Image_Classification

Keywords : AB filter, ADMM, Classification, Hyperspectral image de-noising

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2015

Abstract : In recent years, hyperspectral remote sensing has emerged as a prominent area of research. This has developed a lot of practical solutions to solve the various challenges faced in the field. Noise is one of such issues which deteriorate the quality of information present in the hyperspectral images. In order to address this problem, various preprocessing (denoising) techniques are applied prior to data analysis. In this paper, the proposed method evaluates the effect of Hyperspectral Image (HSI) denoising employing AB filter on optimization based classification which uses Basis Pursuit solved by Alternating Direction Method of Multipliers (ADMM). AVIRIS Indian Pines dataset is used for the experimental study. The efficiency of the proposed technique is proved by a comparative study with other existing preprocessing methods. The experimental result analysis based on visual interpretation and quantitative assessment shows that the proposed method provides better classification results compared to the existing methods. The classification results are assessed by Overall Accuracy, Average accuracy and Kappa coefficient.

Cite this Research Publication : A. C, Haridas, N., Sowmya, and Dr. Soman K. P., “Effect of AB filter denoising on ADMM based Hyperspectral Image Classification”, International Journal of Applied Engineering Research (IJAER), vol. 10, no. 73, pp. 127-131, 2015.

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