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Proceedings Paper

Automatic target recognition via sparse representations
Author(s): Katia Estabridis
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Paper Abstract

Automatic target recognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ∈ Rnxm(n<<m) and x is a sparse vector whose non-zero entries identify the input y. The signal x will be sparse with respect to the dictionary A as long as y is a valid target. The algorithm can reject clutter and background, which are part of the input image. The detection and recognition problems are solved by finding the sparse-solution to the undetermined system y=Ax via Orthogonal Matching Pursuit (OMP) and l1 minimization techniques. Visible and MWIR imagery collected by the Army Night Vision and Electronic Sensors Directorate (NVESD) was utilized to test the algorithm. Results show an average detection and recognition rates above 95% for targets at ranges up to 3Km for both image modalities.

Paper Details

Date Published: 13 May 2010
PDF: 9 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960O (13 May 2010); doi: 10.1117/12.849591
Show Author Affiliations
Katia Estabridis, Naval Air Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; Abhijit Mahalanobis; David P. Casasent; Tien-Hsin Chao; Steven L. Chodos; William E. Thompson, Editor(s)

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