Searching for a fast alternative to KNN for infrared ATR

R. Eaton and M. Snorrason

Proceedings of SPIE Defense & Security, vol. 6566, Orlando, FL (May, 2007)

Automatic target recognition (ATR) using an infrared (IR) sensor is a particularly appealing combination, because an IR sensor can overcome various types of concealment and works in both day and night conditions. We present a system for ATR on low resolution IR imagery. We describe the system architecture and methods for feature extraction and feature subset selection. We also compare two types of classifier, K-Nearest Neighbors (KNN) and Random Decision Tree (RDT). Our experiments test the recognition accuracy of the classifiers, within our ATR system, on a variety of IR datasets. Results show that RDT and KNN achieve comparable performance across the tested datasets, but that RDT requires significantly less retrieval time on large datasets and in high dimensional feature spaces. Therefore, we conclude that RDT is a promising classifier to enable a robust, real time ATR solution.

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