Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery

TL;DR

This paper introduces a deep-learning-based ship detection system using readily available medium resolution optical satellite imagery, offering a cost-effective alternative to SAR-based solutions for maritime monitoring.

Contribution

It develops and evaluates a novel ship detection pipeline tailored for optical satellite data, leveraging automatic AIS-based annotations for large-scale training.

Findings

Effective detection of ships in medium resolution optical imagery.

Comparable performance to SAR-based methods in maritime detection.

Utilizes publicly available satellite data for widespread application.

Abstract

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.