Publication

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.