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Proceedings Paper

Classifying sets of attributed scattering centers using a hash coded database
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Paper Abstract

We present a fast, scalable method to simultaneously register and classify vehicles in circular synthetic aperture radar imagery. The method is robust to clutter, occlusions, and partial matches. Images are represented as a set of attributed scattering centers that are mapped to local sets, which are invariant to rigid transformations. Similarity between local sets is measured using a method called pyramid match hashing, which applies a pyramid match kernel to compare sets and a Hamming distance to compare hash codes generated from those sets. By preprocessing a database into a Hamming space, we are able to quickly find the nearest neighbor of a query among a large number of records. To demonstrate the algorithm, we simulated X-band scattering from ten civilian vehicles placed throughout a large scene, varying elevation angles in the 35 to 59 degree range. We achieved better than 98 percent classification performance. We also classified seven vehicles in a 2006 public release data collection with 100% success.

Paper Details

Date Published: 18 April 2010
PDF: 12 pages
Proc. SPIE 7699, Algorithms for Synthetic Aperture Radar Imagery XVII, 76990Q (18 April 2010); doi: 10.1117/12.855593
Show Author Affiliations
Kerry E. Dungan, The Ohio State Univ. (United States)
Lee C. Potter, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 7699:
Algorithms for Synthetic Aperture Radar Imagery XVII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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