DocumentCode :
3412692
Title :
Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing
Author :
Zare, Alina ; Bchir, Ouiem ; Frigui, Hichem ; Gader, Paul
Author_Institution :
Electr. & Comput. Eng., Univ. of Missouri, Columbia, MO, USA
fYear :
2012
fDate :
Sept. 30 2012-Oct. 3 2012
Firstpage :
2681
Lastpage :
2684
Abstract :
A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian Pines hyperspectral image is used as an example of a piece-wise convex collection of pixels. The convex regions, weights, endmembers and abundances are found using an iterative fuzzy clustering method. Results indicate that the piece-wise convex representation provides endmembers that better represent hyperspectral data sets over methods that use a single convex region.
Keywords :
fuzzy set theory; geophysical image processing; iterative methods; pattern clustering; Indian Pines hyperspectral image; P-COMMEND algorithm; hyperspectral endmember detection; hyperspectral image analysis; iterative fuzzy clustering method; nonconvex hyperspectral data; piece-wise convex representation; piecewise convex multiple model endmember detection; single convex region; spectral unmixing algorithm; Educational institutions; Entropy; Equations; Hyperspectral imaging; Ice; Mathematical model; endmember; hyperspectral; unmixing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1522-4880
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2012.6467451
Filename :
6467451
Link To Document :
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