Hyperspectral texture classification using generalized Markov fields
Hyperspectral texture classification using generalized Markov fields
- New search for: Sarkar, S.
- New search for: Healey, G.
- New search for: Sarkar, S.
- New search for: Healey, G.
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.
Document information
Hyperspectral texture classification using generalized Markov fields
2004-01-01
556523 byte
Conference paper
Electronic Resource
English
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