在富里埃复杂场中的上下文学习用于VHR远程传感图像
IEEE transactions on neural networks and learning systems
|October 4, 2023
概括
我们介绍福里埃复合变压器 (FCT),这是一个高效的模型,用于非常高分辨率的遥感图像分类. FCT显著降低了计算复杂性,同时在分析空中图像方面保持了高准确性.
科学领域:
- 远程传感 (RS) 是一种远程传感技术.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 非常高分辨率 (VHR) 遥感图像分类对于图像分析至关重要.
- 变压器模型擅长捕捉上下文关系,但对于VHR图像来说计算密集.
- 标准变压器的二次复杂性限制了它们对大规模VHR遥感数据的应用.
研究的目的:
- 开发一种高效的基于变压器的模型,用于VHR遥感图像分类.
- 为解决VHR遥感中大图像大小所带来的计算挑战.
- 增强远程传感图像中高阶上下文信息的学习能力.
主要方法:
- 提出了一种高效的复杂自我注意力 (CSA) 机制,通过使用离散里埃转换 (DFT) 来分解自我注意力.
- 利用DFT的并联对称属性来降低计算复杂性 (少于天真SA的一半).
- 引入了一个Logmax函数来稳定梯度传播,并将复杂场中的注意力图正常化,形成富里埃复杂变压器 (FCT).
主要成果:
- 富里埃复合变压器 (FCT) 模型有效地从VHR空中图像中分层学习全球上下文信息.
- 实验证明了FCT的有效性和效率,特别是在VHR遥感图像分类任务中.
- 在显著降低计算成本的情况下,FCT实现了与现有方法相比的或优于现有方法的性能.
结论:
- 拟议的CSA机制和FCT模型为VHR遥感图像分类提供了有效的解决方案.
- FCT成功地克服了标准变压器在高分辨率图像中的计算限制.
- 该方法为推进远程传感图像分析和理解提供了一个有希望的方向.
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