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MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection.
Lichuan Zou1,2, Hong Zhang1, Chao Wang1,2
1Key Laboratory of Digital Earth Science, Aerospace information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel, Switzerland)
|November 25, 2020
Summary
This study introduces an improved generative adversarial network (MW-ACGAN) to create synthetic high-resolution Synthetic Aperture Radar (SAR) ship images. Combining generated and real data significantly boosts deep learning ship detection accuracy, achieving 94% with the Yolo v3 network.
Area of Science:
- Remote Sensing
- Artificial Intelligence
- Computer Vision
Background:
- Deep learning for high-resolution Synthetic Aperture Radar (SAR) ship detection is hindered by limited sample data.
- Existing methods struggle with low detection accuracy when training datasets are small.
Purpose of the Study:
- To develop a novel high-resolution SAR ship detection method for small sample scenarios.
- To improve the generation of realistic SAR ship images and enhance detection accuracy.
Main Methods:
- An improved Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) was developed to generate high-resolution SAR ship images.
- Multi-scale loss terms and output layers were incorporated for generating diverse ship images.
- Generated images were combined with original data to train a Yolo v3 detection network.
Main Results:
- The MW-ACGAN successfully generated multi-scale and multi-class SAR ship images with high confidence (average score 0.91 for ResNet18).
- The Yolo v3 network trained on the composite dataset achieved a detection accuracy of 94%.
- This accuracy is significantly higher than training solely on the original SAR dataset.
Conclusions:
- The proposed MW-ACGAN effectively enhances SAR ship image generation for small sample datasets.
- Combining generated and real data substantially improves deep learning-based ship detection performance.
- The method optimizes the use of original data, leading to superior detection accuracy in high-resolution SAR imagery.
