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Proceedings Paper

Statistical assessment of model fit for synthetic aperture radar data
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Paper Abstract

Parametric approaches to problems of inference from observed data often rely on assumed probabilistic models for the data which may be based on knowledge of the physics of the data acquisition. Given a rich enough collection of sample data, the validity of those assumed models can be assessed in a statistical hypothesis testing framework using any of a number of goodness-of-fit tests developed over the last hundred years for this purpose. Such assessments can be used both to compare alternate models for observed data and to help determine the conditions under which a given model breaks down. We apply three such methods, the (chi) 2 test of Karl Pearson, Kolmogorov's goodness-of-fit test, and the D'Agostino-Pearson test for normality, to quantify how well the data fit various models for synthetic aperture radar (SAR) images. The results of these tests are used to compare a conditionally Gaussian model for complex-valued SAR pixel values, a conditionally log-normal model for SAR pixel magnitudes, and a conditionally normal model for SAR pixel quarter-power values. Sample data for these tests are drawn from the publicly released MSTAR dataset.

Paper Details

Date Published: 27 August 2001
PDF: 10 pages
Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438231
Show Author Affiliations
Michael D. DeVore, Washington Univ. (United States)
Joseph A. O'Sullivan, Washington Univ. (United States)

Published in SPIE Proceedings Vol. 4382:
Algorithms for Synthetic Aperture Radar Imagery VIII
Edmund G. Zelnio, Editor(s)

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