一种基于NAS和变压器的双通道高光谱分类方法.
Wenbin Liu1, Juanjuan Li2, Yiliang Zeng3
1School Of Automation And Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Scientific reports
|October 29, 2025
概括
这项研究介绍了CTmixer-NAS,一种用于高光谱图像 (HSI) 分类的新双通道网络. 它有效地使用神经架构搜索 (NAS) 和变压器网络集成本地和全球信息,提高了分类准确性.
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:
- 变压器网络擅长在全球范围内捕获远程依赖.
- 神经架构搜索 (NAS) 通过专注于局部信息来进行先进的高光谱图像 (HSI) 分类.
- 现有的双通道网络难以同时利用HSI数据中的本地和全球特征,从而限制了分类准确性.
研究的目的:
- 提出CTmixer-NAS,一种用于HSI分类的新双通道网络.
- 同时捕获本地和全球信息,以提高HSI分类的准确性.
- 利用NAS在网络中实现高性能CNN组件的自动设计.
主要方法:
- 开发了CTmixer-NAS,这是一个集成Transformer和CNN分支的双通道网络.
- 在变压器分支中实现了跨自编码层的信息融合.
- 使用NAS自动设计CNN分支机构的最佳CNN结构.
主要成果:
- 在五个基准超光谱数据集中,CTmixer-NAS实现了卓越的性能.
- 提出的方法有效地整合了本地和全球信息,克服了以前方法的局限性.
- 通过NAS驱动的CNN设计增强了分类准确性和泛化性能.
结论:
- CTmixer-NAS通过协同结合变压器和NAS优化的CNN,为HSI分类提供了强大的解决方案.
- 该方法在准确性和概括性方面取得了显著的改进,减少了人工劳动.
- 该方法通过有效处理本地和全球光谱空间信息,为HSI分类设定了新的标准.
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