Abstract
A large number of parameters affect the fundamental performance of automatic target recognition (ATR) systems including sensor, geometry, and scene parameters. Clutter complexity refers to the scene parameter that measures the extent that the objects in the background of the scene are target-like. This paper describes our initial work to understand how to measure clutter complexity and bound ATR performance as a function of this complexity, given that the other parameters are held constant. For this study, we use an unrealistically omnipotent ATR to approximate performance bounds and characterize the clutter complexity for each scene in the COMANCHE FLIR database. Using this characterization, we generate a clutter complexity metric from a number of image processing features and compare the new metric against the performance of an actual ATR.
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