A Systematic Review of Ship Wake Detection Methods in Satellite Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 20; p. 3775
• Main Authors: Mazzeo, Andrea, Renga, Alfredo, Graziano, Maria Daniela
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Datasets; Deep learning; maritime surveillance; Military applications; Motion perception; multi-spectral; Neural networks; Remote sensing; SAR; Satellite imagery; Sea vessels; Sensors; Ship hulls; ship wakes; Surveillance; Target detection; wake detection
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16203775

The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field.