Publication Type:

Conference Proceedings

Source:

International Conference on Soft Computing Systems, ICSCS 2015, AISC Springer Series, Noorul Islam Centre for Higher Education, Kumaracoil; India (2015)

URL:

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

Keywords:

Abundance map, Empirical wavelet transform (EWT), Endmember signature, Hyperspectral Unmixing (HU)

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, V., and Soman, K. P., “Performance enhancement of minimum volume-based hyperspectral unmixing algorithms by empirical wavelet transform”, International Conference on Soft Computing Systems, ICSCS 2015. AISC Springer Series, Noorul Islam Centre for Higher Education, Kumaracoil; India, 2015.