Title :
Endmember extraction from hyperspectral imagery using a parallel ensemble approach with consensus analysis
Author :
Ayuso, F. ; Setoain, J. ; Prieto, M. ; Tenllado, C. ; Tirado, F. ; Plaza, J. ; Plaza, A.
Author_Institution :
Dept. Comput. Archit. Complutense, Univ. of Madrid, Madrid, Spain
Abstract :
We have explored in this paper a framework to test in a quantitative manner the stability of different endmember extraction and spectral unmixing algorithms based on the concept of Consensus Clustering. The idea is to investigate if the sensibility of those algorithms to the number of endmembers can be used to estimate this parameter itself. Preliminary results on synthetic data reveal that the proposed scheme, which can be implemented efficiently in parallel, can compete with state-of-the-art schemes.
Keywords :
pattern clustering; statistical analysis; consensus analysis; consensus clustering; endmember extraction; hyperspectral imagery; parallel ensemble approach; spectral unmixing algorithms; Algorithm design and analysis; Clustering algorithms; Computer architecture; Data mining; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Parameter estimation; Principal component analysis; Stability;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5417725
