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A Bayesian MRF framework for labeling terrain using hyperspectral imaging
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DOI：10.1109/TGRS.2005.846865.png)
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摘要
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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.
Keyword:
Markov-chain Monte Carlo
Markov random field (MRF)
non-Gaussian statistics
terrain labeling
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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