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Proceedings Paper

Description of component model for automated generation of scene statistics and comparison of algorithm performance applied to both natural and hypothetical spectral scenes
Author(s): Andreas F. Hayden; Peter E. Miller; Sabbir A. Rahman; Kim E. Ostrander-O'Brien
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Paper Abstract

There is a need to assess hyperspectral image processing algorithms in a way that does not require applying the algorithm to a large set of spectral scenes. The statistical nature of hyperspectral scenes can be modeled as a set of means and covariances. In this model, each mean-covariance pair describes some physical component of the scene. Modeling the scene in this fashion allows non-gaussian nature of scene to be explored, with the assumption that the scene statistics are linear sums of gaussians. Once this component model of a scene is constructed, filter performance can be assessed quickly by applying the filter to the ensemble of means of covariances. Furthermore, filter performance can be predicted for scenes not yet collected, as scene models may be artificially generated from statistics of physical components. As a validation of the model we generate plots of target probability of detection versus probability of false alarm for natural scenes and models based on those scenes.

Paper Details

Date Published: 16 October 1998
PDF: 10 pages
Proc. SPIE 3438, Imaging Spectrometry IV, (16 October 1998); doi: 10.1117/12.328104
Show Author Affiliations
Andreas F. Hayden, Raytheon Optical Systems, Inc. (United States)
Peter E. Miller, Raytheon Optical Systems, Inc. (United States)
Sabbir A. Rahman, Raytheon Optical Systems, Inc. (United Kingdom)
Kim E. Ostrander-O'Brien, Raytheon Optical Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 3438:
Imaging Spectrometry IV
Michael R. Descour; Sylvia S. Shen, Editor(s)

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