一个具有空间注意力的多尺度变压器,用于高光谱图像分类
Irfan Ahmad1, Ghulam Farooque2, Fazal Hadi3
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China.
Scientific reports
|January 13, 2026
概括
MTSA-Net通过整合多尺度变压器和空间注意力来增强超频谱图像 (HSI) 分类,优于现有方法,特别是在训练数据有限的情况下.
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) 提供了丰富的光谱空间信息,对于准确的分类至关重要.
- 卷积神经网络 (CNN) 是有前途的,但由于固定的规模,在捕获远程依赖和概括方面存在局限性.
- 矛盾的是,CNN中的网络深度会导致HSI分类中的性能下降.
研究的目的:
- 引入MTSA-Net,这是一个用于高光谱图像分类的新框架.
- 为了解决CNN在捕捉远程依赖性和改进概括方面的局限性.
- 开发一种强大而灵活的HSI分类方法,使用多尺度变压器和空间注意力.
主要方法:
- 采用3D和2D卷积层,其次是空间注意力,以关注关键的空间特征.
- 采用多尺度变压器编码器来捕获本地和全球表示,并模拟远程依赖.
- 集成功能融合模块,将不同尺度的功能结合起来,以实现全面的表示.
主要成果:
- 与最先进的方法相比,MTSA-Net 在五个基准HSI数据集上取得了更高的性能.
- 实现了高整体精度:98.84% (印度松树),98.77% (帕维亚大学),99.80% (萨利纳斯山谷),97.84% (休斯顿-13),和95.87% (休斯顿-18).
- 显示出显著的有效性,特别是在训练样本有限的场景中.
结论:
- MTSA-Net提供了一个强大的,灵活的,高性能的框架,用于高光谱图像分类.
- 多尺度变压器和空间注意力的集成有效地捕捉了光谱空间特征和远程依赖.
- 提出的方法显示出有很大的潜力来推进HSI分类,特别是在数据稀缺的情况下.
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