Evolving Filter Banks for ATR in Infrared Images

摘要

This paper describes a method for developing and training a classifier for detecting military vehicles in FLIR (Forward Looking Infrared) imagery. Often image analysis is done via constructing feature vectors from the original two-dimensional image. In this effort, a genetic algorithm is used to evolve a group of linear filters for constructing these feature vectors. Training is performed on collections of target chips and non-target or clutter chips drawn from FLIR image datasets. The evolved filters produce multi-dimensional feature vectors from each sample. First the fitness function for the genetic algorithm rewards maximal separation of target from non-target vectors measured by clustering the two sets and applying a vector space norm. Next, the entire method is adapted to supply feature vectors to a support vector machine classifier (SVM) in order to optimize the SVM's performance, i.e. the genetic algorithm's fitness function rewards effective SVM class distinction. Finally, supplemental features are incorporated into the system, resulting in an improved, hybrid classifier. This classification method is intended to be applicable to a wide variety of target-sensor scenarios.