<title>Multivariate regression as a method for FLIR target detection</title>

Abstract

I promote using an alternative philosophy for the design of infrared-target-detection algorithms. This philosophy focuses on finding first and eliminating natural clutter from a scene, followed by finding and preserving candidate targets in that scene. The reverse approach is the most common adopted one in the infrared ATR (automatic target recognition) community. This alternative is appealing because it should significantly reduce the amount of out-of-context information to be processed by a classifier. I show how to apply sensor domain knowledge, common sense, and multivariate regression to the problem of infrared target detection. A proof-of-principle experiment and its results are discussed.