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It is a challenging problem to detect and recognize targets on complexlarge-scene Synthetic Aperture Radar (SAR) images. Recently developed deeplearning algorithms can automatically learn the intrinsic features of SARimages, but still have much room for improvement on large-scene SAR images withlimited data. In this paper, based on learning representations and multi-scalefeatures of SAR images, we propose an efficient and robust deep learning basedtarget detection method. Especially, by leveraging the effectiveness ofadversarial autoencoder (AAE) which influences the distribution of theinvestigated data explicitly, the raw SAR dataset is augmented into an enhancedversion with a large quantity and diversity. Besides, an auto-labeling schemeis proposed to improve labeling efficiency. Finally, with jointly trainingsmall target chips and large-scene images, an integrated YOLO network combiningnon-maximum suppression on sub-images is used to realize multiple targetsdetection of high resolution images. The numerical experimental results on theMSTAR dataset show that our method can realize target detection and recognitionon large-scene images accurately and efficiently. The superior anti-noiseperformance is also confirmed by experiments.

Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images