Publication Type:

Journal Article

Source:

Indian Journal of Science and Technology, Volume 8, Issue 24, Number 24 (2015)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944460374&partnerID=40&md5=c81c172f41d959f396675b7537388032

Abstract:

Hyper spectral unmixing of data has become an indispensable technique in remote sensing zone. Spectral Unmixing is defined as the source separation of a mixed pixel. The fundamental sources are termed as endmembers and percentage of the source content is known as abundances. This paper demonstrates the effect of Variational Mode Decomposition (VMD) on hyper spectral unmixing algorithms based on geometrical minimum volume approaches. The proposed method is experimented on standard hyper spectral dataset namely, cuprite. The effectiveness of the proposed method is subjected to evaluation, based on the standard quality metric namely, Root Mean Square Error (RMSE). The experimental result analysis shows that, the proposed technique enhance the performance of hyper spectral unmixing algorithms based on the geometrical minimum volume based approaches.

Cite this Research Publication

P. G. Mol, Sowmya, V., and Soman, K. P., “Performance enhancement of minimum volume based hyper spectral unmixing algorithms by variational mode decomposition”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.