SPAN: Strong Scattering Point Aware Network for Ship Detection and Classification in Large-Scale SAR Imagery

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.1188-1204
• Main Authors: Sun, Yuanrui, Wang, Zhirui, Sun, Xian, Fu, Kun
• Format: Article
• Language: English
• Subjects: Boxes; Classification; Classifiers; Coders; Deep learning; Detection; Detectors; Distribution; Feature extraction; Image classification; Image processing; Imagery; Interpolation; Machine learning; Marine vehicles; Modules; Radar imaging; Radar polarimetry; SAR; SAR (radar); Scattering; Ships; strong scattering points; Synthetic aperture radar; Task analysis; Texture recognition
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2022.3142025

Ship detection and classification in synthetic aperture radar (SAR) images play a vital role for wide applications. Due to the unique SAR imaging mechanism, ship detection and classification tasks have faced numerous challenges, such as land interference, image defocus, and noise. Many detectors and classifiers have been presented to handle these problems. However, the general deep learning-based detectors and classifiers lack the combination of SAR characteristics, which leads to poor performance. Compared with optical images, SAR images lack the texture information of ships, which brings great difficulties to the recognition task. To address the above issues, a novel deep learning-based ship detection and classification network combined with scattering characteristics is proposed in this article. First, to accurately locate ships in large-scale SAR images, this article designs a strong scattering point aware network (SPAN) by capturing the strong scattering points that existed in the ship area. SPAN recognizes the ship category according to their distribution characteristics. Second, to compensate for the feature loss caused by the down-sampling operation, this article designs a more suitable resolution recovery module to replace the bilinear interpolation method. Third, a region of interest automatic generation module is proposed to fully utilize the axis-align feature of oriented proposal boxes and the sufficient information of horizontal proposal boxes. Furthermore, the classification encoder module extracts the distribution feature of scattering points to classify SAR ships. Finally, the comprehensive experiments in the large-scale dataset for ship detection and classification in SAR images (LDSD) demonstrate the superior performance of the proposed method.