PROCEEDINGS ARTICLE | May 5, 2009
Proc. SPIE. 7335, Automatic Target Recognition XIX
KEYWORDS: Data mining, Infrared imaging, Weapons, Detection and tracking algorithms, Data modeling, Databases, Mining, Target recognition, Automatic target recognition, Statistical modeling
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.
