We present an approach for discriminating among dierent classes of imagery in a scene. Our intended application is the detection of small watercraft in a littoral environment where both targets and land- and sea-based clutter are present. The approach works by training dierent overcomplete dictionaries to model the dierent image classes. The likelihood ratio obtained by applying each model to the unknown image is then used as the discriminating test statistic. We rst demonstrate the approach on an illustrative test problem and then apply the algorithm to short-wave infrared imagery with known targets.

Date Published: 17 May 2012
PDF: 7 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920N (17 May 2012); doi: 10.1117/12.920725

Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)