Extracting ship and heading from Sentinel-2 images using convolutional neural networks with point and vector learning

Xiunan LI; Peng CHEN; Jingsong YANG; Wentao AN; Dan LUO; Gang ZHENG; Aiying LU

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Extracting ship and heading from Sentinel-2 images using convolutional neural networks with point and vector learning

Physics | Updated：2025-02-19

- Extracting ship and heading from Sentinel-2 images using convolutional neural networks with point and vector learning
- Journal of Oceanology and Limnology Vol. 43, Issue 1, Pages: 16-28(2025)
- Affiliations：
  
  1. Ocean College, Zhejiang University, Zhoushan 316021, China
  2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  3. National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
- Author bio：
  
  chenpeng@sio.org.cn
  jsyang@sio.org.cn
- Funds：
- DOI：
  CLC：
- Received：12 December 2023，
  
  Published：01 January 2025
- Accepted：
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LI Xiunan,CHEN Peng,YANG Jingsong,et al.Extracting ship and heading from Sentinel-2 images using convolutional neural networks with point and vector learning[J].Journal of Oceanology and Limnology,2025,43(01):16-28.
DOI：

LI Xiunan,CHEN Peng,YANG Jingsong,et al.Extracting ship and heading from Sentinel-2 images using convolutional neural networks with point and vector learning[J].Journal of Oceanology and Limnology,2025,43(01):16-28. DOI：

摘要

Abstract

Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications. However

current deep learning-based methods primarily focus on ship position detection

while the detection of ship wakes relies on traditional non-deep learning approaches

which often underperform in complex marine environments. We proposed a novel

simple

and efficient method called Point-Vector Net. The proposed method leverages convolutional neural networks (CNN) for feature extraction and subsequently integrates multi-scale features to generate high-resolution feature maps. In the final stage

ship positions and headings are represented using a combination of points and vectors. Comparative experiments with results from automatic identification system (AIS) reports demonstrate that our method achieved impressive performance in two-class ship target detection

with an average precision of 96.4%

recall rate of 94.3%

and an F1 score of 95.2%. Notably

the average heading error was 3.3°. The proposed model achieved a practical inference speed (FPS>30)

and the average processing time for inferring a large-scale Sentinel-2 remote sensing image was 11.4 s.

关键词

Keywords

references

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Related Author

Xiunan LI

Peng CHEN

Jingsong YANG

Dan LUO

Gang ZHENG

Aiying LU

Tian MA

Qing XU

Related Institution

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources

College of Marine Technology/Sanya Oceanographic Institution, Ocean University of China

Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center

National Key Laboratory of Intelligent Spatial Information

Marine Science and Technology College, Zhejiang Ocean University

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