Publication Type : Conference Paper
Publisher : 6th International Conference on Advances in Computing and Communications , ICACC-2016
Source : 6th International Conference on Advances in Computing and Communications , ICACC-2016, Elsevier, Volume 93, Rajagiri School of Engineering & Technology, 6-8 September 2016, p.416-423 (2016)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985995666&partnerID=40&md5=2172903e8cb8d218e8b9d5a6990a42a4
Keywords : basis pursuit, Classification (of information), Convex optimization, De-noising, Hyperspectral image classification, image classification, Image denoising, Independent component analysis, Least Square, Least squares approximations, Legendre, Spectroscopy
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication
Year : 2016
Abstract : Hyperspectral images contain a huge amount of spatial and spectral information so that, almost any type of Earth feature can be discriminated from any other feature. But, for this classification to be possible, it is to be ensured that there is as less noise as possible in the captured data. Unfortunately, noise is unavoidable in nature and most hyperspectral images need denoising before they can be processed for classification work. In this paper, we are presenting a new approach for denoising hyperspectral images based on Least Square Regularization. Then, the hyperspectral data is classified using Basis Pursuit classifier, a constrained L1 minimization problem. To improve the time requirement for classification, Alternating Direction Method of Multipliers (ADMM) solver is used instead of CVX (convex optimization) solver. The method proposed is compared with other existing denoising methods such as Legendre-Fenchel (LF), Wavelet thresholding and Total Variation (TV). It is observed that the proposed Least Square (LS) denoising method improves classification accuracy much better than other existing denoising techniques. Even with fewer training sets, the proposed denoising technique yields better classification accuracy, thus proving least square denoising to be a powerful denoising technique. © 2016 The Authors. Published by Elsevier B.V.
Cite this Research Publication : S. Srivatsa, Ajay, A., Chandni, C. K., Sowmya, and Dr. Soman K. P., “Application of Least Square Denoising to Improve ADMM Based Hyperspectral Image Classification”, in 6th International Conference on Advances in Computing and Communications , ICACC-2016, Rajagiri School of Engineering & Technology, 6-8 September 2016, 2016, vol. 93, pp. 416-423.