LGFUNet:基于全球信息的多尺度本地特征的SAR图像中的水提取网络
Xiaowei Bai1, Yonghong Zhang1, Jujie Wei1,2
1Chinese Academy of Surveying and Mapping, Beijing 100036, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
通过更好地处理阴影和复杂的边界,LGFUNet模型改进了基于深度学习的SAR图像的水提取. 这种新方法显著提高了连续水体绘图的准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 人工智能的人工智能
背景情况:
- 深度学习模型在准确地从SAR图像中提取水体时面临挑战,特别是将它们与阴影区分开来,并划出复杂的连续水界.
- 现有的方法往往在细节特征提取和在下采样过程中保持空间信息方面扎.
研究的目的:
- 提出LGFUNet模型,用于从SAR图像中提取水.
- 克服当前深度学习方法的局限性,特别是解决与阴影的混,改善复杂水体边界的提取.
主要方法:
- LGFUNet模型集成了使用Swin-Transformer进行全球信息学习的编码器-解码器架构.
- 包含一个用于多尺度特征提取的DECASPP模块和一系列LGFF模块,以弥合语义差距并减轻空间信息丢失.
主要成果:
- 与使用Sentinel-1 SAR数据的U-Net,Swin-UNet和SCUNet++相比,LGFUNet模型在提取水方面表现优越.
- 该模型有效地减少了山影和水体之间的混,并改善了复杂的连续水体细节的提取.
结论:
- 拟议的LGFUNet模型在SAR图像中提取水方面取得了重大进展.
- 它的架构有效地捕捉了全球背景和当地细节,从而更准确,更可靠地绘制水资源的地图.
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