Analysis of the impact of feature-enhanced SAR imaging on ATR performance

We present an evaluation of the impact of a recently proposed feature-enhanced synthetic aperture radar (SAR) imaging technique on automatic target recognition (ATR) performance. We run recognition experiments using conventional and feature-enhanced SAR images of military targets, in three different classifiers. The first classifier is template-based. The second classifier makes a decision through a likelihood test, based on Gaussian models for reflectivities. The third classifier is based on extracted locations of the dominant target scatterers. The experimental results demonstrate that feature-enhanced SAR imaging can improve the recognition performance, especially in scenarios involving reduced data quality or quantity.