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Publication Type : Journal Article
Publisher : International Journal of Scientific Engineering Research
Source : International Journal of Scientific & Engineering Research, Volume 5, Issue 7, p.315-319 (2014)
Keywords : Hyperspectral images, Invariant features, Kappa Coefficient, Overall accuracy, Perona Malik Diffusion, Scattering transform, Support Vector Machine
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : Yes
Year : 2014
Abstract : In this paper, we applied scattering transform approach for the classification of hyperspectral images. This method integrates features, such as the translational and rotational invariance features for image classification. The classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labelled examples typically available for learning. The scattering transform technique is validated with two standard hyperspectral datasets i.e, SalinasA_Scene and Salinas_Scene. The experimental result analysis proves that the applied scattering transform method provides high classification accuracy of 99.35% and 89.30% and kappa coefficients of 0.99 and 0.88 for the mentioned hyperspectral image dataset respectively.
Cite this Research Publication : P. S. Ashitha, Sowmya, and Dr. Soman K. P., “Classification of hyperspectral images using scattering transform”, International Journal of Scientific & Engineering Research, vol. 5, no. 7, pp. 315-319, 2014.