MFMINet:多式融合和跨层交互网络,用于对高分辨率遥感图像进行语义细分
Junjie Chen1,2, Feiting Wang1,2, Jiangtao Fang1,2
1School of Computer Technology and Application, Qinghai University, 251, Ningda Road, Xining, Qinghai 810016, China.
iScience
|February 12, 2026
概括
本研究介绍了MFMINet,这是一个用于远程传感图像分析的新型深度学习网络. 它有效地融合多式联网数据,以提高城市规划和土地覆盖地图的语义细分精度.
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:
- 深度卷积神经网络 (CNN) 在遥感中推进语义细分.
- 单模成像面临着光谱模糊性和几何混,阻碍了准确的土地覆盖分类.
- 多源数据融合提供了潜力,但在有效利用互补信息方面面临挑战.
研究的目的:
- 提出一种新型的多式融合和多层交互网络 (MFMINet),用于增强遥感图像的语义细分.
- 解决单模成像的局限性,改善对光谱相似的土地覆盖类型的歧视.
- 有效地整合来自不同数据源的互补信息.
主要方法:
- 开发了MFMINet,这是一个双向编码器解码器网络,包含多式联通交层融合模块 (MCFM).
- 集成了一个自我注意模块 (SAM) 用于远程依赖捕获和一个功能增强模块 (FEM) 用于自适应区块选择 (变压器/CNN).
- 采用双空间意识模块 (DSAM) 来解决下方采样效应和处理多层次上下文信息.
主要成果:
- 在ISPRS Vaihingen和波茨坦数据集上,MFMINet实现了卓越的性能.
- 欧盟 (mIoU) 的平均交叉点分分别达到89.96%和88.24%.
- 拟议的模块有效地改善了特征融合和空间上下文理解.
结论:
- 拟议的MFMINet在多式远程传感图像语义细分方面表现出显著的有效性.
- 整合了MCFM,SAM,FEM和DSAM模块,提高了该模型利用互补数据的能力.
- 这种方法为复杂的土地覆盖面分析和城市规划应用提供了可靠的解决方案.
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