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Proceedings Paper

Implementation of a multiscale Bayesian classification approach for hyperspectral terrain categorization
Author(s): Patricia K. Murphy; Marc A. Kolodner
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Paper Abstract

In this paper we discuss the implementation of a multi-scale Bayesian classifier that operates on hyperspectral data in both the spatial as well as the spectral domain, the Sequential Maximum A Posteriori (SMAP) classifier. Class assignments are modeled as a Markov random process in multi-resolution scale. For applications such as terrain categorization, the SMAP algorithm results in an improved classification that is less noisy than spectral-only based techniques. In addition, for highly overlapping classes, the SMAP significantly outperforms conventional discriminant function approaches. We present the results of the SMAP classifier on several hyperspectral datasets and discuss an extension of the algorithm to perform shading and sub-pixel analyses.

Paper Details

Date Published: 8 November 2002
PDF: 10 pages
Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); doi: 10.1117/12.451620
Show Author Affiliations
Patricia K. Murphy, Johns Hopkins Univ. (United States)
Marc A. Kolodner, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 4816:
Imaging Spectrometry VIII
Sylvia S. Shen, Editor(s)

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