Data Augmentation of Ship Wakes in SAR Images Based on Improved CycleGAN

• Published in: Shanghai jiao tong da xue xue bao Vol. 29; no. 4; pp. 702 - 711
• Main Authors: Yan, Congqiang, Guo, Zhengyun, Cai, Yunze
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.08.2024; Springer Nature B.V
• Subjects: Architecture; Computer Science; Data augmentation; Electrical Engineering; Engineering; Generative adversarial networks; Image enhancement; Image quality; Life Sciences; Materials Science; Radar imaging; Synthetic aperture radar; Target detection; 注意力机制; A; 合成孔径雷达 (SAR); synthetic aperture radar (SAR); data augmentation; attention mechanism; 舰船尾迹; 数据增强; ship wake; TP391.4; cycle-consistent generative adversarial network (CycleGAN); 循环一致性生成对抗网络 (CycleGAN); cycle-consistent generative adversarial network(CycleGAN); synthetic aperture radar(SAR)
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-024-2746-8

The study on ship wakes of synthetic aperture radar (SAR) images holds great importance in detecting ship targets in the ocean. In this study, we focus on the issues of low quantity and insufficient diversity in ship wakes of SAR images, and propose a method of data augmentation of ship wakes in SAR images based on the improved cycle-consistent generative adversarial network (CycleGAN). The improvement measures mainly include two aspects: First, to enhance the quality of the generated images and guarantee a stable training process of the model, the least-squares loss is employed as the adversarial loss function; Second, the decoder of the generator is augmented with the convolutional block attention module (CBAM) to address the issue of missing details in the generated ship wakes of SAR images at the microscopic level. The experiment findings indicate that the improved CycleGAN model generates clearer ship wakes of SAR images, and outperforms the traditional CycleGAN models in both subjective and objective aspects.