从基于非均采样和半监督学习的高分辨率遥感图像提取河流
Kun Wang1,2,3, Lin Han2,4, Liangzhi Li5,6
1School of Computer Science and Technology, Weinan Normal University, Wei Nan, Shaanxi, China.
Scientific reports
|January 31, 2026
概括
这项研究引入了一种新的非统一采样和基于图形的半监督学习方法,用于准确的河流提取. 它有效地利用未标记的数据,显著提高了洪水预警等关键应用程序的准确性和IOU指标.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理信息系统 地理信息系统
背景情况:
- 精确的河流提取对于水资源管理和防灾准备至关重要.
- 现有的编码器解码器网络遭受数据稀缺和细节损失.
- 利用未标记的数据是改善河流提取模型的关键.
研究的目的:
- 开发一种有效的河流提取方法,解决细节丢失和数据稀缺问题.
- 通过使用未标记的数据来增强现有的深度学习模型.
- 为了提高河流绘图的准确性和稳定性.
主要方法:
- 一个不统一的抽样战略,优先考虑高频率的河边地区.
- 在非均采样后,对特征融合进行双线插值.
- 基于图形的半监督学习 (SSL) 以利用未标记的数据集.
主要成果:
- 改进了Unet,Linknet和DeeplabV3模型的准确性和联盟交叉点 (IoU).
- 显著提升像素精度 (5.0%) 和IOU (9.3%) 在未标记数据上使用SSL.
- 在Gaofen-2和OpenEarthMap数据集上证明了稳定性和通用性.
结论:
- 拟议的非统一采样和SSL框架有效地提高了河流采集.
- 该方法提供了一个强大的解决方案,用于在遥感应用中利用未标记的数据.
- 这种方法为需要精确划分河流网络的应用提供了有价值的工具.
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