DocumentCode
576156
Title
Sparse endmember extraction and demixing
Author
Ehler, M. ; Hirn, M.
Author_Institution
Helmholtz Zentrum Munchen, German Res. Center for Environ. Health, Neuherberg, Germany
fYear
2012
fDate
22-27 July 2012
Firstpage
1385
Lastpage
1388
Abstract
A novel algorithm for endmember extraction is presented. The approach follows the linear mixture model for hyperspectral data. Endmembers are identified based on sparsity consideration. Theoretical and experimental results suggest the potential of the method.
Keywords
feature extraction; geophysical image processing; image representation; image resolution; minerals; set theory; terrain mapping; abundance maps; chemical composition; endmember set; hyperspectral data; linear mixture model; mixture-of-materials representation; multispectral image set; multispectral measurements; sparse endmember demixing; sparse endmember extraction; sparsity consideration; Dictionaries; Hyperspectral imaging; Materials; Noise; Optimization; Signal processing algorithms; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location
Munich
ISSN
2153-6996
Print_ISBN
978-1-4673-1160-1
Electronic_ISBN
2153-6996
Type
conf
DOI
10.1109/IGARSS.2012.6351278
Filename
6351278
Link To Document