Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

Abstract

This paper presents a new supervised classification approach for automated target recognition (ATR) in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.