JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

A SAR Ship Detection Method Based on Adversarial Training

  • 0Naval Submarine Academy, Qingdao 264001, China.
Sensors (basel, Switzerland) +

|

July 13, 2024

|

July 13, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Adversarial training enhances synthetic aperture radar (SAR) ship detection by generating perturbed images, improving detector robustness and accuracy. This method significantly boosts performance on benchmark datasets compared to traditional data augmentation techniques.

Area Of Science

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background

  • Synthetic Aperture Radar (SAR) ship detection is crucial for maritime surveillance.
  • Existing detectors struggle with generalization due to limited SAR image volumes.
  • Traditional data augmentation methods offer minimal improvements in detection accuracy.

Purpose Of The Study

  • To develop an adversarial training approach for generating synthetic SAR images to improve ship detection.
  • To enhance the generalization ability and robustness of SAR ship detectors.
  • To overcome the limitations of conventional data augmentation in SAR imaging.

Main Methods

  • Adversarial training is employed to generate new training samples by adding perturbations to SAR images.
  • Batch normalization is separated for clean and perturbed samples to prevent performance degradation.
  • K-step average perturbation and one-step gradient descent optimize the training process.
  • Simultaneous perturbation and selection of high-loss samples improve adaptability to challenging scenarios.

Main Results

  • The proposed adversarial training method significantly improves SAR ship detection performance.
  • Average Precision (AP) gains of 8%, 10%, and 17% were achieved on SSDD, SAR-Ship-Dataset, and AIR-SARShip, respectively.
  • The method demonstrates superior results compared to traditional data augmentation techniques.

Conclusions

  • Adversarial training is an effective strategy for enhancing SAR ship detection by generating robust features.
  • The proposed approach improves detector adaptability and performance across various datasets.
  • This method offers a promising solution for improving the generalization ability of SAR ship detectors in diverse maritime environments.

Keywords: