DocumentCode :
2711867
Title :
Analysis of hyperspectral data with diffusion maps and Fuzzy ART
Author :
Xu, Rui ; Du Plessis, Louis ; Damelin, Steven ; Sears, Michael ; Wunsch, Donald C., II
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ. of Sci. & Technol., Rolla, MO, USA
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
3390
Lastpage :
3397
Abstract :
The presence of large amounts of data in hyperspectral images makes it very difficult to perform further tractable analyses. Here, we present a method of analyzing real hyperspectral data by dimensionality reduction using diffusion maps. Diffusion maps interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain an efficient representation of data geometric descriptions. A neural network clustering theory, Fuzzy ART, is further applied to the reduced data to form clusters of the potential minerals. Experimental results on a subset of hyperspectral core imager data show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples.
Keywords :
ART neural nets; Markov processes; data analysis; eigenvalues and eigenfunctions; fuzzy set theory; geometry; image sampling; matrix algebra; pattern clustering; Markov matrix eigenfunction; diffusion map; dimensionality reduction; fuzzy ART; geometric data description; hyperspectral data analysis; hyperspectral image; image sampling; neural network clustering theory; Africa; Data analysis; Frequency; Hyperspectral imaging; Minerals; Multispectral imaging; Neural networks; Optical imaging; Pixel; Subspace constraints;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5178910
Filename :
5178910
Link To Document :
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