DocumentCode :
3598574
Title :
Hyperspectral texture classification using generalized Markov fields
Author :
Sarkar, Subhadip ; Healey, Glenn
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Irvine, CA, USA
Volume :
1
fYear :
2004
Abstract :
We present a generalized random field model in a random environment to classify hyperspectral textures. The model generalizes traditional random field models by allowing the spatial interaction parameters of the field to be random variables. Principal component analysis is used to reduce the dimensionality of the data set to a small number of spectral bands that capture almost all of the energy in the original hyperspectral textures. Using the model we obtain a compact feature vector that efficiently computes within and between band information. Using a set of hyperspectral samples, we evaluate the performance of this model for classifying textures and compare the results with other approaches.
Keywords :
Markov processes; feature extraction; image classification; image texture; principal component analysis; random processes; spectral analysis; dimensionality reduction; feature vector; generalized Markov Fields; generalized random field model; hyperspectral texture classification; performance evaluation; principal component analysis; random variables; spatial interaction parameters; spectral bands; Computer vision; Energy capture; Gray-scale; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Image texture analysis; Pixel; Principal component analysis; Random variables;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2158-4
Type :
conf
DOI :
10.1109/CVPR.2004.1315064
Filename :
1315064
Link To Document :
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