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Publication Type : Conference Proceedings
Publisher : AISC Springer Series
Source : International Conference on Soft Computing Systems (ICSCS 2015), Advances in Intelligent Systems and Computing, AISC Springer Series, Volume 397, Noorul Islam Centre for Higher Education, Kumaracoil; India, p.251-256 (2016)
Keywords : Abundance map, Empirical wavelet transform (EWT), Endmember signature, Hyperspectral Unmixing (HU)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication, Communication
Year : 2016
Abstract : Hyperspectral unmixing of data has become one of the essential processing steps for crop classification. The endmembers to be extracted from the data are statistically dependent either in the linear or nonlinear form. The primary focus of this paper is on the effect of empirical wavelet transform (EWT) on hyperspectral unmixing algorithms based on the geometrical minimum volume approaches. The proposed method is experimented on the standard hyperspectral dataset, namely Cuprite. The performance analysis of proposed approach is eval- uated based on the standard quality metric called root mean square error (RMSE). The experimental result analysis shows that our proposed technique based on EWT improves the performance of hyperspectral unmixing algorithms based on the geometrical minimum volume approaches.
Cite this Research Publication : P. G. Mol, Sowmya, and Dr. Soman K. P., “Performance enhancement of minimum volume-based hyperspectral unmixing algorithms by empirical wavelet transform”, International Conference on Soft Computing Systems (ICSCS 2015), Advances in Intelligent Systems and Computing, vol. 397. AISC Springer Series, Noorul Islam Centre for Higher Education, Kumaracoil; India, pp. 251-256, 2016.