This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre–Fenchel transformation denoises each band in the data while preserving the edge information. To overcome the issue of high data volume, inter-band block correlation coefficient technique followed by singular value decomposition and QR decomposition is utilized to reduce the dimension of hyperspectral image without affecting the critical information. The preprocessed data are classified using kernel-based libraries, namely GURLS and LibSVM. Performance of these techniques is evaluated with accuracy assessment measures. The experiment was performed on five datasets. Experimental analysis shows that the proposed denoising technique increases the classification accuracy. In the case of Indian Pines data, with 10% of the training data, the classification accuracy is improved from 83.5 to 97.3%. And also, dimensionality reduction technique gives good classification accuracy even with 50% reduction in the number of bands. The classification accuracy of the Salinas-A and Pavia University data is 99.4 and 94.6% with the 50% dimensionally reduced (100 and 50 bands, respectively) number of bands. The bands extracted by the dimensionality reduction technique using the denoised hyperspectral data differ from that of the hyperspectral data without denoising. This emphasizes the importance of denoising the dataset before applying dimensionality reduction technique. In case of Pavia University, the band numbers above 50 (out of 100 bands) which were not informative bands before denoising are selected as informative bands by dimension reduction technique after denoising . © 2017 The Natural Computing Applications Forum
R. Reshma, Sowmya, V., and Soman, K. P., “Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification”, Neural Computing and Applications, pp. 1-10, 2017.