Adaptive Ship Detection From Optical to SAR Images

Recent advances in synthetic aperture radar (SAR) ship detection have witnessed remarkable success by using large-scale annotated datasets. However, the annotation of SAR images requires strong domain-specific expertise, significantly hindering the prompt adoption of modern object detectors in this regime. Compared to SAR data, optical data in geoscience are considerably easier to label. Motivated by this, we investigate a new and challenging problem—adaptive ship detection—with the goal of enhancing ship detection performance on SAR images by leveraging knowledge transferred from optical images. Considering the large distributional discrepancy between the source (optical) and target (SAR) domains, we present OmniAdapt, a novel framework that progressively narrows the distance between the two types of images at the pixel, feature, and classifier levels. Specifically, OmniAdapt consists of three main modules, target-like generation module (TLGM), multifeature alignment module (MFAM), and common specific decomposition module (CSDM). TLGM minimizes the visual disparity by infusing the target-domain style into the source domain. MFAM aligns local- and global-level feature representations in an adversarial manner. Finally, CSDM decomposes the classifier into two independent components, that is, the domain-common component and the domain-specific component, and promotes the recognition ability of the former via regularization learning. Experimental results demonstrate the effectiveness of the proposed method.

I. Introduction

The objective of synthetic aperture radar (SAR) ship detection is to identify and locate ships in SAR images, which has a wide range of applications in many areas such as maritime surveillance and military missions. Benefiting from the rapid development of deep neural networks, modern detection algorithms have achieved unprecedented improvement. However, this improvement is dependent on the supervised training of large-scale data, which dramatically aggravates the burden of data collection and annotation. This requirement proves to be costly and time-consuming, particularly when dealing with SAR images featuring intricate scenes and cluttered backgrounds. Compared with SAR, the data of optical remote sensing is easier to obtain and label. Therefore, we aim to utilize optical remote images to train a SAR ship detector to alleviate the problem of insufficient SAR data and labels. However, the mismatch of data distribution will make the detection results unsatisfactory.