关闭域差距:来自合成无人机数据的伪标签可以实现现实世界洪水细分吗?
Georgios Simantiris1, Konstantinos Bacharidis1,2, Costas Panagiotakis1,2
1Department of Management Science and Technology, Hellenic Mediterranean University, 72100 Agios Nikolaos, Greece.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究引入了一种新方法,用于创建合成无人机 (UAV) 洪水图像,使用文本到图像合成和inpainting. 这些合成数据使洪水细分模型的稳定性提高了1-7%.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 分段模型与各种洪水图像作斗争.
- 缺乏注释的真实世界数据阻碍了模型的概括.
- 无人机 (UAV) 图像提供了宝贵的洪水监测数据.
研究的目的:
- 开发一种用于生成和过合成无人机洪水图像的新方法.
- 增强图像细分模型的泛化能力,用于洪水检测.
- 通过有效地结合真实和合成训练数据来提高模型的稳定性.
主要方法:
- 使用文本到图像合成和图像绘制用于合成数据生成.
- 采用无监督的伪标签来自动创建细分面具.
- 在功能空间中实现异常值检测,用于合成数据过和现实性增强.
主要成果:
- 合成数据与训练表现中的真实数据非常相匹配.
- 结合真实和合成数据,模型的稳定性提高了1%至7%.
- 快速设计显著影响了生成图像的视觉真实性.
结论:
- 拟议的框架有效地产生现实的合成无人机洪水图像.
- 合成数据增强增强了洪水细分模型的概括性和稳定性.
- 该方法为遥感应用中的数据稀缺性提供了可行的解决方案.
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