Automatic target recognition (ATR) for unexploded ordnance (UXO) detection and classification using sonar data of opportunity from open oceans survey sites is an open research area. The goal here is to develop ATR spanning real-aperture and synthetic aperture sonar imagery. The classical paradigm of anomaly detection in images breaks down in cluttered and noisy environments. In this work we present an upgraded and ultimately more robust approach to object detection and classification in image sensor data. In this approach, object detection is performed using an in-situ weighted highlight-shadow detector; features are generated on geometric moments in the imaging domain; and finally, classification is performed using an Ada-boosted decision tree classifier. These techniques are demonstrated on simulated real aperture sonar data with varying noise levels.