SAR Ship Detection Based on Deep Domain Adaptation with Limited Samples

Abstract

Ship detection in synthetic aperture radar (SAR) images have important applications in military and civil fields. Nonetheless, SAR images usually suffer from the disadvantage of having limited labeled samples. This is undoubtedly a challenge for the deep learning based methods, which require a large number of samples for training. To solve this problem, many researchers have applied the domain adaptation (DA) methods for the SAR ship detection tasks, which use optical images as the source domain and transform optical images to SAR domain directly. However, there is large difference between SAR and optical images. Hence, the deep learning detector may not be able to learn the target features accurately when the transformed images are used for training. In order to solve above problem, a novel ship detection method with limited labeled samples is proposed, whose training and test process is mainly implemented in the optical domain. In the training stage, optical images are used to pre-train the model. Here, the transformed optical images from SAR images are used to further fine-tune the pre-trained model. Accordingly, in the test stage, SAR images are transformed to optical domain and input in the model to obtain the detection results. Experimental results based on the real dataset demonstrate the effectiveness of the proposed method for SAR ship detection.