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Proceedings Paper

Automated rapid training of ATR algorithms
Author(s): Jonah McBride; Jessica Lowell; Magnús Snorrason; Ross Eaton; John Irvine
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Paper Abstract

Computer vision methods, such as automatic target recognition (ATR) techniques, have the potential to improve the accuracy of military systems for weapon deployment and targeting, resulting in greater utility and reduced collateral damage. A major challenge, however, is training the ATR algorithm to the specific environment and mission. Because of the wide range of operating conditions encountered in practice, advanced training based on a pre-selected training set may not provide the robust performance needed. Training on a mission-specific image set is a promising approach, but requires rapid selection of a small, but highly representative training set to support time-critical operations. To remedy these problems and make short-notice seeker missions a reality, we developed Learning and Mining using Bagged Augmented Decision Trees (LAMBAST). LAMBAST examines large databases and extracts sparse, representative subsets of target and clutter samples of interest. For data mining, LAMBAST uses a variant of decision trees, called random decision trees (RDTs). This approach guards against overfitting and can incorporate novel, mission-specific data after initial training via perpetual learning. We augment these trees with a distribution modeling component that eliminates redundant information, ignores misrepresentative class distributions in the database, and stops training when decision boundaries are sufficiently sampled. These augmented random decision trees enable fast investigation of multiple images to train a reliable, mission-specific ATR. This paper presents the augmented random decision tree framework, develops the sampling procedure for efficient construction of the sample, and illustrates the procedure using relevant examples.

Paper Details

Date Published: 4 May 2009
PDF: 8 pages
Proc. SPIE 7335, Automatic Target Recognition XIX, 73350P (4 May 2009); doi: 10.1117/12.818881
Show Author Affiliations
Jonah McBride, Charles River Analytics, Inc. (United States)
Jessica Lowell, Charles River Analytics, Inc. (United States)
Magnús Snorrason, Charles River Analytics, Inc. (United States)
Ross Eaton, General Dynamics Robotic Systems (United States)
John Irvine, The Charles Stark Draper Lab., Inc. (United States)

Published in SPIE Proceedings Vol. 7335:
Automatic Target Recognition XIX
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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