Extended fractal feature for first-stage SAR target detection

Abstract
The Extended Fractal (EF) feature has been shown to lower the false alarm rate for the focus of attention (FOA) stage of a synthetic aperture radar (SAR) automatic target recognition (ATR) system. The feature is both contrast and size sensitive, and thus, can discriminate between targets and many types of cultural clutter at the earliest stages of the ATR. In this paper we modify the EF feature so that one can 'tune' the size sensitivity to the specific targets of interest. We show how to optimize the EF feature using target chip data from the public MSTAR database. We demonstrate improvements in performance for FOA algorithms that include the new feature by comparing the receiver operating characteristic (ROC) curves for all possible combinations of FOA algorithms incorporating EF, two-parameter CFAR, and variance features. Finally, we perform timing experiments on the fused detector to demonstrate the feasibility for implementation of the detector in a real system.