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Proceedings Paper

Unsupervised learning in hyperspectral classifiers using hidden Markov models
Author(s): Vikram Jayaram; Bryan Usevitch
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Paper Abstract

Hyperspectral data represents a mixture of several component spectra from many classifiable sources. The knowledge of the contributions of the underlying sources to the recorded spectra is valuable in many remote sensing applications. Traditional Hyperspectral classification and segmentation algorithms have used Markov random field (MRF) based estimation in recent investigations. Although, this method reflects plausible local, spatial correlation in the true scene, it is limited to using supervised learning schemes for parameter estimation. Besides, the expectation-maximization (EM) for the hidden MRF is considerably more difficult to realize due to the absence of a closed form formulation. In this paper, we propose a hidden Markov model (HMM) based probability density function (PDF) classifier for reduced dimensional feature space. Our approach uses an unsupervised learning scheme for maximum-likelihood (ML) parameter estimation that combines both model selection and estimation in a single algorithm. The proposed method accurately models and synthesizes the approximate observations of the true data in a reduced dimensional feature space.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340F (27 April 2009); doi: 10.1117/12.820325
Show Author Affiliations
Vikram Jayaram, The Univ. of Texas at El Paso (United States)
Bryan Usevitch, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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