J Shanghai Jiaotong Univ Sci ›› 2024, Vol. 29 ›› Issue (4): 702-711.doi: 10.1007/s12204-024-2746-8
鄢丛强1,2, 郭正玉3,4, 蔡云泽 1,2
接受日期:
2023-09-26
出版日期:
2024-07-14
发布日期:
2024-07-14
YAN Congqiang1,2 (鄢丛强), GUO Zhengyun3,4 (郭正玉), CAI Yunze1,2∗∗ (蔡云泽)
Accepted:
2023-09-26
Online:
2024-07-14
Published:
2024-07-14
摘要: 合成孔径雷达(SAR)图像的舰船尾迹研究在海洋船舶目标的探测中具有重要意义。本研究针对SAR图像舰船尾迹数据样本数量少和多样性不足的问题,提出一种基于改进的循环一致性生成对抗网络(CycleGAN)的SAR图像舰船尾迹数据增强方法。改进措施主要包括两个方面:第一,采用最小二乘损失作为对抗损失函数,提高了生成图像的质量,稳定了模型的训练过程;第二,在生成器的解码器中嵌入卷积块注意力模块(CBAM),在微观层面上解决了生成的SAR图像舰船尾迹中信息丢失的问题。实验结果表明,改进后的CycleGAN模型能生成更清晰的SAR图像舰船尾迹样本,在主观和客观方面都优于传统的CycleGAN模型。
中图分类号:
鄢丛强1,2, 郭正玉3,4, 蔡云泽 1,2. 基于改进CycleGAN的SAR图像舰船尾迹数据增强[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 702-711.
YAN Congqiang1,2 (鄢丛强), GUO Zhengyun3,4 (郭正玉), CAI Yunze1,2∗∗ (蔡云泽). Data Augmentation of Ship Wakes in SAR Images Based on Improved CycleGAN[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 702-711.
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