TARGET RECOGNITION FOR MULTI-ASPECT SAR IMAGES WITH FUSION STRATEGIES

Two fusion strategies for target recognition using multi-aspect synthetic aperture radar (SAR) images are presented for recognizing ground vehicles in MSTAR database. Due to radar cross-section variability, the ability to discriminate between targets varies greatly with target aspect. Multi-aspect images of a given target are used to support recognition. In this paper, two fusion strategies for target recognition using multi-aspect SAR images are proposed, which are data fusion strategy and decision fusion strategy. The recognition performance sensitivity to the number of images and the aspect separations is analyzed for those two target recognition strategies. The two strategies are also compared with each other in probability of correct classification and operating efficiency. The experimental results indicate that if we have a small number of multi-aspect images of a target and the aspect separations between those images are proper, the probability of correct classification obtained by the two proposed strategies can be advanced significantly compared with that obtained by the method using single image.