SWFormer:用于混合模式高频谱分类的随机Windows卷积变压器
概括
本研究介绍了用于增强超光谱图像 (HSI) 和LiDAR分类的静态窗口变压器 (SWFormer). SWFormer 改进了特征提取,并减少了计算负载,以实现更准确的远程传感数据解释.
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数据的联合分类提供了增强的解释,特别是高度信息.
- 变压器架构在HSI和LiDAR分类方面表现有前途,但在局部/多尺度特征提取和高计算成本方面存在困难.
研究的目的:
- 提出一种新的静态窗口变压器 (SWFormer) 框架,以解决现有的变压器架构在HSI和LiDAR分类中的局限性.
- 改进从HSI数据中同时提取局部空间和多尺度光谱信息.
- 为了降低基于变压器的分类模型所需的计算能力.
主要方法:
- 开发了独立的空间和光谱特征投影网络,在混合模式异质数据上使用并行特征提取.
- 实现了多尺度的条形卷积,结合了用于灵活的局部-全球非线性特征映射的变压器策略.
- 引入了一个随机的窗户变压器结构,用于稀疏的窗户修剪,减少冗余和注意力参数.
- 设计了一个plug-and-play功能聚合模块,以适应性地最大限度地减少模态功能之间的域偏移.
主要成果:
- 拟议的SWFormer框架有效地提取不同维度的代表性感知特征.
- 多尺度条纹卷积和随机窗口变压器结构增强了局部-全球非线性特征图的构建.
- 功能聚合模块成功地将HSI和LiDAR数据之间的语义差距降至最低,改善了融合的功能表示.
- 在三个数据集上的实验证明了SWFormer在分类任务中的有效性.
结论:
- 通过克服天真变压器架构的局限性,SWFormer在超光谱图像和LiDAR数据分类方面取得了重大进展.
- 该框架为复杂的遥感数据的特征提取和融合提供了更高效和有效的方法.
- SWFormer的创新结构带来了更好的分类性能和更少的计算需求.
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