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一种基于施罗丁格桥梁网络和多尺度特征融合的未配对SAR-to-optical图像转换方法
Jinyu Wang1, Haitao Yang2, Yu He1
1Space Engineering University, Beijing, 101416, China.
Scientific reports
|November 7, 2024
概括
本研究介绍了一种新的方法,用于将合成孔径雷达 (SAR) 转换为光学 (S2O) 图像,使用施罗丁格桥梁框架. 这种方法提高了图像翻译质量,即使使用有限的,未配对的数据.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 合成孔径雷达 (SAR) 到光学 (S2O) 图像的翻译有助于信息提取.
- 训练S2O模型在未配对的数据集中具有挑战性.
- 现有的方法在有限的,未配对的数据上扎.
研究的目的:
- 开发一种高效的SAR-to-optical图像转换方法.
- 为了解决对未配对数据的训练的局限性.
- 为了提高从SAR数据生成的光学图像的质量.
主要方法:
- 采用了基于施罗丁格桥梁的转型框架.
- 引入了一种多尺度轴余模块 (MARM) 用于多尺度特征融合.
- 设计了一个生成器和区分器,以实现高效的S2O转换.
主要成果:
- 在SAR-to-optical转换中实现了优越的生成质量.
- 在SEN1-2数据集上证明了有效性.
- 与CycleGAN.com相比,FID指标得到了42.05%的改进.
结论:
- 提出的基于施罗丁格桥梁的方法为SAR-to-optical图像转换提供了卓越的性能.
- 为了改进翻译,MARM有效地融合了多个尺度的特征.
- 该方法是高效和有效的,特别是在有限的未配对数据.
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