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.
cited By 0; Conference of 6th International Conference On Advances In Computing and Communications, ICACC 2016 ; Conference Date: 6 September 2016 Through 8 September 2016; Conference Code:131418
S. Srivatsa, Ajay, A., Chandni, C. K., Sowmya V., and 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.