By Topic

Classifying Vehicles in Wide-Angle Radar Using Pyramid Match Hashing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Dungan, K.E. ; Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA ; Potter, L.C.

We present a fast, scalable method to simultaneously register and classify vehicles in circular synthetic aperture radar imagery. The method is robust to 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 binary hash codes, 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°-59° range. We achieved better than 98% classification performance. Similar performance is demonstrated for a seven class task using airborne radar measurements.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:5 ,  Issue: 3 )