多天气域变换器:一种全面的多天气转移LLM代理,用于处理空中图像处理中的域变换.
Yubo Wang1, Ruijia Wen1, Hiroyuki Ishii1
1Department of Modern Mechanical Engineering, Waseda University, Tokyo 169-8555, Japan.
Journal of imaging
|November 26, 2025
概括
随着天气变化而发生的域移动会降低遥感模型的质量. 多气候域变换器使用合成数据和人工智能来适应模型的新条件,没有额外的注释,提高了各种各样的空中图像的性能.
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:
- 远程传感中的深度学习模型面临性能下降,原因是来自不同照明,大气条件和场景变化的领域转移.
- 适应空中图像细分模型是困难的,因为高成本和多种天气条件的注释训练数据的稀缺性.
研究的目的:
- 开发一个全面的多天气域转移系统,多天气域转换器,用于增强单域空中图像到各种天气条件.
- 为了实现域调整而不需要额外的繁的数据注释,由大型语言模型 (LLM) 代理协调.
主要方法:
- 使用虚幻引擎生成一个合成数据集,使用不同的天气条件 (多云,雾,尘埃).
- 实现隐藏空间风格转移模型,从真实空中数据集创建替代域版本.
- 开发一个多模式的雪景扩散模型,使用LLM辅助的场景描述器来整合雪景元素.
- 将这些方法集成到由LLM代理管理的工具库中,用于自动选择和执行工具.
主要成果:
- 由于天气变化的域移动显著降低了空中图像细分模型的性能.
- 拟议的多天气域变换器有效地调整模型,使其在转移的域中 (例如,多云,雾,尘埃,雪地) 性能良好.
- 该系统保持了原始域中的模型有效性,同时在增强的天气条件下提高了性能.
结论:
- 多气候DomainShifter提供了一个强大的解决方案,用于解决远程传感图像分析中的域移动挑战.
- 该LLM协调系统成功地产生了各种天气条件,提高了模型的适应性和性能,而无需大量的手动注释.
- 这种方法显著提高了深度学习模型在现实世界,可变遥感场景中的弹性和适用性.
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