By Topic

A Bayesian MRF framework for labeling terrain using hyperspectral imaging

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

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.

Published in:

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