Boosting Ship Detection in SAR Images With Complementary Pretraining Techniques

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.8941-8954
• Main Authors: Bao, Wei, Huang, Meiyu, Zhang, Yaqin, Xu, Yao, Liu, Xuejiao, Xiang, Xueshuang
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Benchmarks; Common representation learning; Complementarity; Datasets; Deep learning; Detection; Detectors; False alarms; Feature extraction; Imaging techniques; Machine learning; Marine vehicles; Neural networks; Optical detectors; Optical imaging; optical ship detector (OSD) pretraining; optical-SAR matching (OSM) pretraining; Radar detection; Radar imaging; Radar polarimetry; SAR (radar); Sensors; ship detection; Ships; Synthetic aperture radar; weighted boxes fusion (WBF)
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3109002

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 hard to obtain a good ship detector because of different imaging perspectives and geometry. In this article, to resolve the problem of inconsistent imaging perspectives between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique to transfer 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 OSM task. Finally, observing that the OSD pretraining-based SSD has a better recall on sea area while the OSM pretraining-based SSD 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 three representative convolutional neural network-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 the 2020 Gaofen challenge.