RST-Net:基于双分支编码器结构的远程传感图像的语义细分网络
Na Yang1, Chuanzhao Tian1,2, Xingfa Gu1,3
1College of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
通过融合本地和全球特征,RST-Net改善了远程传感图像的语义细分. 这种网络增强了空间细节和对象细分的准确性,优于现有的方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 高分辨率的遥感图像需要有效的语义细分.
- 当前的方法难以将全球和本地特征融合在一起,导致失去了依赖关系和模糊的细节.
- 对多尺度对象细分的有限适应性是一个关键挑战.
研究的目的:
- 提出RST-Net,这是一个用于高分辨率遥感图像的新语义细分网络.
- 为了解决不充分的特征融合和多尺度对象细分的局限性.
- 增强地方空间和全球上下文信息的提取和融合.
主要方法:
- 开发了具有双分支编码器的RST-Net:用于本地特征的CNN分支 (ResNeXt-50) 和用于全球环境的变频器 (ST) 分支.
- 集成了一个多尺度特征增强模块 (MSFEM),使用形和深度可分离的卷积来进行动态特征聚合.
- 在跳过连接中集成了一个残余动态特征融合 (RDFF) 模块,以改善编码器-解码器特征交互.
主要成果:
- 在Vaihingen和波茨坦数据集上,RST-Net实现了高性能.
- 实现了对联盟 (MIoU) 的平均交叉点得分,在Vaihingen上为77.04%和在Potsdam上为79.56%.
- 在语义细分精度和细节保存方面取得了显著的改进.
结论:
- RST-Net有效地克服了用于远程传感图像分割的全球和本地功能融合的局限性.
- 拟议的网络显示出强大的适应性,以多个规模的对象细分.
- 在复杂的遥感场景中,RST-Net验证了其有效性和有前途的性能.
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