一个密集的金字塔式残余网络与一个并联的光谱空间注意力机制,用于高光谱图像分类
Yunlan Guan1,2,3, Zixuan Li2, Nan Wang2
1Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究引入了一种新的超光谱图像分类 (HSIC) 方法,该方法使用了一种新的并联光谱空间注意模块 (TAM) 和密集金字塔残余模块 (DPRM). 这种方法可以实现高精度和复杂数据集的改进处理速度.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 对于高光谱图像分类 (HSIC) 有效.
- 在HSIC的主要挑战包括提高分类准确性,降低计算成本,提高概括能力.
- 现有的方法往往难以高效处理大规模的超频谱数据集.
研究的目的:
- 提出一种用于高光谱图像分类 (HSIC) 的新方法.
- 通过使用高级深度学习模块来提高分类准确性和处理速度.
- 为了自动选择显著的光谱和空间特征来改进HSIC.
主要方法:
- 一个合的光谱空间注意模块 (TAM) 设计用于自动特征选择.
- 构建了一个密集的金字塔残余模块 (DPRM),包含三个残余单元 (RUs) 和密集的连接.
- 扩展的卷积结构被纳入,以改善细纹理和特征感知.
主要成果:
- 拟议的方法在四个公共数据集上实现了高分类准确性:帕维亚大学 (99.60%),萨利纳斯 (99.95%),茶农 (99.81%) 和WHU-Hi-HongHu (99.84%).
- 观察到处理速度的显著改善,特别是对于像WHU-Hi-HongHu.Hu这样的大型数据集.
- 每个时代的训练和测试时间分别为53s和1.28s.
结论:
- 与现有方法相比,新方法在高光谱图像分类 (HSIC) 中表现优越.
- 集成TAM和DPRM有效地提高了HSIC的准确性和效率.
- 该方法为高频谱图像的准确和快速分析提供了一个有前途的解决方案.
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