13 May 2015 Mixture of factor analyzers models of appearance manifolds for resolved SAR targets
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Proceedings Volume 9475, Algorithms for Synthetic Aperture Radar Imagery XXII; 94750G (2015); doi: 10.1117/12.2176397
Event: SPIE Defense + Security, 2015, Baltimore, Maryland, United States
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.
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Tarek Abdelrahman, Emre Ertin, "Mixture of factor analyzers models of appearance manifolds for resolved SAR targets", Proc. SPIE 9475, Algorithms for Synthetic Aperture Radar Imagery XXII, 94750G (13 May 2015); doi: 10.1117/12.2176397; http://dx.doi.org/10.1117/12.2176397
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KEYWORDS
Synthetic aperture radar

Data modeling

Error analysis

Feature extraction

Signal to noise ratio

Scattering

Factor analysis

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