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.
翻译:在本文中,我们介绍了从欧空局哨兵-2和行星实验室鸽子星座获得的低成本中分辨率卫星光学图像的船舶探测管道,与基于合成孔径雷达(SAR)图像的现有解决办法相比,这种光学卫星图像在地球上任何地点都可以轻易获得,在海洋领域利用不足,我们开发了一种基于最先进的深层学习天体探测方法的船舶探测方法,该方法是在一个大型数据集的基础上开发和评价的,该数据集是在自动识别系统的帮助下收集和自动附加说明的。