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Proceedings Paper

Mixture of factor analyzers models of appearance manifolds for resolved SAR targets
Author(s): Tarek Abdelrahman; Emre Ertin
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Paper Abstract

We study the problem of target identification from Synthetic Aperture Radar (SAR) imagery. Target classification using SAR imagery is a challenging problem due to large variations of target signature as the target aspect angle changes. Previous work on modeling wide angle SAR imagery has shown that point features, extracted from scattering center locations, result in a high dimensional feature vector that lies on a low dimensional manifold. In this paper we use rich probabilistic models for these target manifolds to analyze classification performance as a function of Signal-to-noise ratio (SNR) and Bandwidth. We employ Mixture of Factor Analyzers (MoFA) models to approximate the target manifold locally, and use error bounds for the estimation and analysis of classification error performance. We compare our performance predictions with the empirical performance of practical classifiers using simulated wideband SAR signatures of civilian vehicles.

Paper Details

Date Published: 13 May 2015
PDF: 8 pages
Proc. SPIE 9475, Algorithms for Synthetic Aperture Radar Imagery XXII, 94750G (13 May 2015); doi: 10.1117/12.2176397
Show Author Affiliations
Tarek Abdelrahman, The Ohio State Univ. (United States)
Emre Ertin, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 9475:
Algorithms for Synthetic Aperture Radar Imagery XXII
Edmund Zelnio; Frederick D. Garber, Editor(s)

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