An Algorithm of Typical Target Recognition for Airborne SAR Based on Deep Learning

In multi-resolution circumstances, multi-class typical target recognition based on synthetic aperture radar (SAR) pictures is an important aspect of SAR image information interpretation. Based on the YOLO-v4 network model, a real-time detection processing architecture is provided for multi-scene cross-resolution detection on real airborne platforms using the existing properties of airborne SAR images and target information. The problem of a vast span of target scales in multi-resolution SAR sceneries is addressed by double detection of several types of targets, data augmentation of training sets with low sample data volume, and combining the same type of target information after picture segmentation. The experimental results show that this method can achieve 82. 8% mean average precision (MAP) values on the relevant airborne SAR dataset for six types of targets (airports, bridges, overpasses, cars, armed vehicles and aircraft), which has important implications for the detection and recognition of more types of targets in subsequent airborne SAR complex scenes.