Genetic algorithm based feature selection for target detection in SAR images

A genetic algorithm (GA) approach is presented to select a set of features to discriminate the targets from the natural clutter false alarms in SAR images. Four stages of an automatic target detection system are developed: the rough target detection, feature extraction from the potential target regions, GA based feature selection and the final Bayesian classification. A new fitness function based on minimum description length principle (MDLP) is proposed to drive GA and it is compared with three other fitness functions. Experimental results show that the new fitness function outperforms the other three fitness functions and the GA driven by it selected a good subset of features to discriminate the targets from clutters effectively. q 2003 Elsevier Science B.V. All rights reserved.