基于双分支变压器的高光谱图像和LiDAR数据的联合分类
Qingyan Wang1, Binbin Zhou1, Junping Zhang2
1School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了一种双分支跨变压器网络,用于使用高光谱图像 (HSI) 和光检测和测距 (LiDAR) 数据改进土地覆盖分类. 该模型有效地融合了光谱和空间特征,优于现有方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 机器学习 机器学习
背景情况:
- 单模数据限制了复杂情景中的分类性能.
- 多模式遥感数据集成面临的挑战包括样本差异和特征相关性.
- 不同质的数据源之间的有效交互对于增强分类至关重要.
研究的目的:
- 为共同的土地覆盖分类提出一个新的双分支跨变压器特征融合网络.
- 为了有效地整合高光谱图像 (HSI) 和光检测和测距 (LiDAR) 数据.
- 克服单一模式方法的局限性,提高分类准确性.
主要方法:
- 一个双分支网络利用卷积运算符用于空间特征和变压器架构用于远程依赖.
- 一个改进的自我注意机制,用于模式内特征聚合 (HSI光谱,LiDAR升高).
- 一个基于交叉关注的功能融合模块,用于跨模式信息集成.
主要成果:
- 拟议的网络有效地融合了来自HSI和LiDAR数据的光谱和空间信息.
- 三个数据集的实验结果表明,与现有方法相比,其性能优越.
- 交叉注意机制促进了模式之间的互补信息交换.
结论:
- 双分支交叉变压器网络为多模式遥感分类提供了强大的方法.
- 通过联合利用HSI和LiDAR数据与有效的融合,提高了土地覆盖分类的准确性.
- 该模型解决了用于表面观测的多式联运数据集成的关键挑战.
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