Implementation of a multiscale Bayesian classification approach for hyperspectral terrain categorization

Patricia K. Murphy; Marc A. Kolodner

doi:10.1117/12.451620

8 November 2002 Implementation of a multiscale Bayesian classification approach for hyperspectral terrain categorization

Patricia K. Murphy, Marc A. Kolodner

Author Affiliations +

Proceedings Volume 4816, Imaging Spectrometry VIII; (2002) https://doi.org/10.1117/12.451620
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

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.

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Patricia K. Murphy and Marc A. Kolodner "Implementation of a multiscale Bayesian classification approach for hyperspectral terrain categorization", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); https://doi.org/10.1117/12.451620

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