- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
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| Citation: | PAN Lei, GUO Yushi, LI Hengchao, WANG Weiye, LI Zechen, MA Tianyu. SAR Image Generation Method via PCGAN for Ship Detection[J]. Journal of Southwest Jiaotong University, 2024, 59(3): 547-555. doi: 10.3969/j.issn.0258-2724.20210630
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Most of existing synthetic aperture radar (SAR) image generation methods fail to generate SAR images and their detection labels simultaneously. A position-based conditional generative adversarial network (PCGAN) is constructed for SAR ship image generation and target detection. Firstly, ship position information is used as a constraint and a detection label to restrict its position in the generated image. Then, the Wasserstein distance is further introduced to stabilize the training process of PCGAN. Finally, the generated SAR images and their corresponding labels are applied for the end-to-end training of YOLOv3, so as to realize the cooperative learning of data enhancement and ship detection, and further obtain the diversified ship data more coupled with the practical application of ship detection. Experimental results conducted on HRSID dataset illustrate that PCGAN can generate clear and robust SAR ship data, and the accuracy of ship detection can be improved by up to 1.01%, thus verifying the proposed method.
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