Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery
This addresses maritime surveillance by leveraging underutilized optical imagery, though it appears incremental as it applies existing deep learning methods to a new data source.
The paper tackles ship detection using low-cost medium resolution satellite optical imagery from ESA Sentinel-2 and Planet Labs Dove, developing a deep-learning-based object detection method evaluated on a large-scale dataset automatically annotated with AIS data.
In this paper, we present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations. This optical satellite imagery is readily available for any place on Earth and underutilized in the maritime domain, compared to existing solutions based on synthetic-aperture radar (SAR) imagery. We developed a ship detection method based on a state-of-the-art deep-learning-based object detection method which was developed and evaluated on a large-scale dataset that was collected and automatically annotated with the help of Automatic Identification System (AIS) data.