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Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery

arXiv 2104.13923 ↗First posted 28 Apr 2021cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 28 Apr 2021.

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8 of 29 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  5. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  6. 2018Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networkscited by 54
  7. 2017Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Predictioncited by 19
  8. 2016Fine-to-coarse Knowledge Transfer For Low-Res Image Classificationcited by 11

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