Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 397, Kumaracoil; India, p.251-256 (2016)

ISBN:

9788132226697

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955238631&partnerID=40&md5=c2a0dbdd3ac174ba406d412bb1d5e021

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. © Springer India 2016.

Notes:

cited By 0; Conference of International Conference on Soft Computing Systems, ICSCS 2015 ; Conference Date: 20 April 2015 Through 21 April 2015; Conference Code:160689

Cite this Research Publication

P. G. Mol, Sowmya, V., and Soman, K. P., “Performance enhancement of minimum volume-based hyperspectral unmixing algorithms by empirical wavelet transform”, Advances in Intelligent Systems and Computing, vol. 397, pp. 251-256, 2016.