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4 May 2011 Target classification in synthetic aperture radar using map-seeking circuit technology
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Abstract
Conventional target recognition approaches for SAR include template matching and feature-based classification. However, unlike visual imagery, Synthetic Aperture Radar (SAR) presents a unique challenge in that many attributes, such as scattering centers, are extremely pose dependent and wink in and out with even minor viewing geometry changes. This work implements a highly efficient biologically-inspired 3D template-based approach, the Map Seeking Circuit (MSC) algorithm, for target recognition in SAR. Instead of exhaustively searching a high dimensional state space, the MSC algorithm efficiently searches a superposition hypersurface to estimate target location and 3D pose. Results are shown from applying the algorithm to real SAR datasets.
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Cameron K. Peterson, Patricia Murphy, and Pedro Rodriguez "Target classification in synthetic aperture radar using map-seeking circuit technology", Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 805113 (4 May 2011); https://doi.org/10.1117/12.884015
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