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混合Mamba-Graph融合与多阶段伪标签精细化,用于半监督的超光谱LiDAR分类
Khanzada Muzammil Hussain1, Keyun Zhao1, Sachal Perviaz1
1School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种新的混合Mamba-Graph融合网络 (HMGF-Net),用于半监督的超光谱LiDAR分类,在有限的标记数据中显著提高了准确性. 该方法通过先进的伪标签改进有效地利用未标记的数据,优于现有的最先进的方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 超光谱图像 (HSI) 和LiDAR数字表面模型 (DSM) 的半监督分类是具有挑战性的,因为标记数据和特征异质性有限.
- 现有的方法难以应对类内变异性,并有效地融合各种光谱和海拔数据.
研究的目的:
- 为超光谱和LiDAR数据开发一个先进的半监督分类框架.
- 解决标记像素的稀缺问题,提高遥感分类准确性.
主要方法:
- 拟议的混合Mamba-Graph融合网络 (HMGF-Net) 集成了光谱空间HSI骨干,LiDAR CNN编码器,Mamba风格的序列块和图形融合模块.
- 实施多阶段伪标签精制 (MS-PLR) 管道,用于逐步过,平滑和重量化伪标签.
- 利用3D-2D卷积,状态空间序列块用于远程依赖模型,以及异质像素图用于信息传播.
主要成果:
- 在三个基准数据集中,HMGF-Net在整体准确性,平均准确性和Cohen的kappa方面取得了持续的收益.
- 与八个最先进的基线相比,表现出优越的性能,特别是在低标签制度 (10%的标签像素) 中.
- 拟议的方法有效地最大化了多式远程传感中的大量未标记样本的实用性.
结论:
- 序列建模 (Mamba) 和图形推理之间的协同作用对于有效的多式联络遥感分类至关重要.
- 精心设计的伪标签改进策略对于具有有限标签数据的半监督学习至关重要.
- HMGF-Net为具有挑战性的超光谱LiDAR分类任务提供了强大的解决方案.
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