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Robust Learning with Noisy Ship Trajectories by Adaptive Noise Rate Estimation.

Haoyu Yang1, Mao Wang1, Zhihao Chen1

  • 1Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

This study introduces A-JoCoR, a novel method for ship trajectory classification that adapts to noisy Automatic Identification System (AIS) data without needing prior noise rate information. It significantly enhances classification accuracy for maritime security.

Keywords:
AIS datadeep learninglabel noisenoise rate adaptive learningreal-world datarobustnesstrajectory classification

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Area of Science:

  • Maritime security
  • Machine learning
  • Data science

Background:

  • Ship trajectory classification is crucial for maritime analysis and security.
  • Illicit vessels falsify Automatic Identification System (AIS) data, introducing label noise that degrades classification accuracy.
  • Existing sample selection methods for noisy labels often require pre-determined noise rates.

Purpose of the Study:

  • To develop a robust ship trajectory classification method that can handle label noise adaptively.
  • To address the limitations of existing methods that require prior knowledge of the noise rate.
  • To improve the accuracy and reliability of ship classification in the presence of manipulated AIS data.

Main Methods:

  • Proposed a noise rate adaptive learning mechanism operating without prior conditions.
  • Integrated this mechanism with the JoCoR (joint training with co-regularization) robust training paradigm.
  • Developed a novel noise rate adaptive learning robust training paradigm named A-JoCoR.

Main Results:

  • A-JoCoR demonstrated effective adaptive learning of the data noise rate during the training process.
  • Experimental results on real-world ship trajectory data confirmed the effectiveness of A-JoCoR.
  • Significant improvements in classification performance were observed compared to the original JoCoR method.

Conclusions:

  • A-JoCoR offers a robust solution for ship trajectory classification with noisy AIS data.
  • The method successfully adapts to unknown noise rates, enhancing maritime security governance.
  • A-JoCoR represents a significant advancement in learning from noisy labels for maritime applications.