  Deep learning methods have made significant progress in ship detection in
synthetic aperture radar (SAR) images. The pretraining technique is usually
adopted to support deep neural networks-based SAR ship detectors due to the
scarce labeled SAR images. However, directly leveraging ImageNet pretraining is
hardly to obtain a good ship detector because of different imaging perspective
and geometry. In this paper, to resolve the problem of inconsistent imaging
perspective between ImageNet and earth observations, we propose an optical ship
detector (OSD) pretraining technique, which transfers the characteristics of
ships in earth observations to SAR images from a large-scale aerial image
dataset. On the other hand, to handle the problem of different imaging geometry
between optical and SAR images, we propose an optical-SAR matching (OSM)
pretraining technique, which transfers plentiful texture features from optical
images to SAR images by common representation learning on the optical-SAR
matching task. Finally, observing that the OSD pretraining based SAR ship
detector has a better recall on sea area while the OSM pretraining based SAR
ship detector can reduce false alarms on land area, we combine the predictions
of the two detectors through weighted boxes fusion to further improve detection
results. Extensive experiments on four SAR ship detection datasets and two
representative CNN-based detection benchmarks are conducted to show the
effectiveness and complementarity of the two proposed detectors, and the
state-of-the-art performance of the combination of the two detectors. The
proposed method won the sixth place of ship detection in SAR images in 2020
Gaofen challenge.
