DSWFNet:空间和波形特征的双分支融合,用于从遥感图像中提取道路
Kunlun Zhang1,2, Azizan As'arry3, Xibing Shen4
1Advanced Science and Technology Research Institute, Beibu Gulf University, Qinzhou, China.
Scientific reports
|December 30, 2025
概括
本研究介绍了DSWFNet,这是一个新的双分支框架,用于从遥感图像中提取道路. 通过融合空间和频域特征,DSWFNet提高了道路检测准确性和结构连续性.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 从遥感图像中提取道路对于城市规划和监测至关重要.
- 由于仅依赖空间域特征,现有的方法与复杂的道路结构作斗争.
研究的目的:
- 开发一种先进的道路提取方法,克服空间域方法的局限性.
- 改进精细图像细节和复杂道路拓的建模.
主要方法:
- 提出了一个双分支框架,DSWFNet,整合空间和频率域特征.
- 使用离散波形变换 (DWT) 进行频域分析.
- 实现了多尺度坐标通道注意力 (MSCCA),增强频域通道注意力 (EFDCA) 和双向交叉注意力模块 (BCAM),用于特征增强和融合.
主要成果:
- 在马萨诸塞州的数据集上,DSWFNet实现了66.07%的IOU和79.57%的F1,表现优于OARENet.
- 在CHN6-CUG数据集上,DSWFNet的IOU达到70.76%和F1的82.88%,超过了领先的基线.
结论:
- 拟议的DSWFNet有效地融合了空间和频域特征,以实现优质的道路开采.
- 注意机制和双分支架构显著提高了对道路目标和结构连续性的敏感性.
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