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A Bayesian MRF framework for labeling terrain using hyperspectral imaging

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2 Author(s)
R. Neher ; Dept. of Math. & Stat., Air Force Inst. of Technol., Wright Patterson AFB, OH, USA ; A. Srivastava

Studies of hyperspectral images point to non-Gaussian statistics of pixels values, and consequently, standard Gaussian models may not perform well in hyperspectral image analysis. This paper presents novel probability models that capture non-Gaussian statistics of hyperspectral images, and uses them in automated classification of terrain sites. After the data are preprocessed using standard dimension-reduction tools, we use: 1) a nonparametric density estimate for capturing spectral variation at each site and 2) two parametric families-generalized Laplacian and Bessel K form-to capture non-Gaussian statistics of difference pixels. Assuming an Ising-type prior on site labels, favoring a smooth classification, we formulate a Markov random field-maximum a posteriori estimation problem and use a Markov chain to estimate site classifications. Results are presented from application of this framework to Washington, DC Mall and Indian Springs rural area datasets.

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IEEE Transactions on Geoscience and Remote Sensing  (Volume:43 ,  Issue: 6 )