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19 July 2000 Nonlinear feature extraction and Bayesian mixture model approaches to target classification using MMW ISAR imagery: a preliminary study
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Abstract
The problem we are addressing is one of generalization: given training data characterizing a set of targets (in specific configurations), how can we design a classifier that is robust to changes in target configuration and can generalize to other targets of the same generic class? The specific problem is identifying land vehicles from an inverse synthetic aperture radar image of the target. Issues in data modeling, experimental design and exploratory data analysis are discussed. Two complementary approaches are described: one that seeks to capture structure in the high- dimensional data space by projecting the data nonlinearly to a reduced dimensional feature space prior to classification; and a second that models the data in the data space using a Bayesian mixture model approach. Preliminary results for the mixture model approach are presented.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adrian Britton, Keith D. Copsey, Guy T. Maskall, Andrew R. Webb, and Karl West "Nonlinear feature extraction and Bayesian mixture model approaches to target classification using MMW ISAR imagery: a preliminary study", Proc. SPIE 4033, Radar Sensor Technology V, (19 July 2000); https://doi.org/10.1117/12.391844
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