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科学领域:

  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 变压器在高光谱图像 (HSI) 分类中面临计算挑战 (二次复杂性),导致错误和记忆问题.
  • 马巴架构为远距离建模提供线性计算效率,但在HSI数据中的空间光谱特征提取方面存在困难.

研究的目的:

  • 介绍DS-Mamba,一个新的深度可分离的Mamba模型,用于增强HSI分类.
  • 解决基本Mamba在提取HSI数据的空间和光谱特征方面的局限性.

主要方法:

  • 设计深度空间Mamba (DSpaM) 和深度光谱Mamba (DSpeM) 块,使用深度可分离的卷积和Mamba.
  • 整合了一个特征增强模块,用于改进空间光谱特征提取和融合.
  • 在特征改进的分类模块中使用了有效通道注意力 (ECA).

主要成果:

  • DS-Mamba 实现了高整体精度:96.54% (帕维亚大学),91.52% (汉川),和94.89% (休斯顿).
  • 在分类性能方面表现优于几种基于变压器的先进方法.
  • 在帕维亚大学数据集中,仅用137.74K参数和12.52G FLOP,证明了显著较低的计算成本.

结论:

  • DS-Mamba有效地提取高精度的空间光谱特征用于HSI分类.
  • 拟议的模型为基于变压器的方法提供了一个计算效率高的替代方案,用于HSI分析.
  • 在超光谱成像中,DS-Mamba显示出强大的实际应用潜力.