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张量器多次空间表示用于远程传感 图像混合噪声清除
IEEE transactions on neural networks and learning systems
|October 28, 2025
概括
本研究介绍了用于远程传感图像 (RSI) 无噪声的张量多子空间表示 (TenMSR). 通过捕捉复杂的数据结构,TenMSR有效地消除混合噪声,优于现有的单个子空间方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 图像处理 图像处理
- 计算机视觉 计算机视觉
背景情况:
- 遥感图像 (RSI) 无声化对于数据质量至关重要.
- 当前的方法通常假定一个子空间,这对于复杂的RSI来说是不够的.
- 波长差异和时间变化需要先进的无声化方法.
研究的目的:
- 提出一种新的 Tensor 多次空间表示 (TenMSR) 方案,用于在 RSIs 中消除混合噪声.
- 为了准确地描述RSI数据的内在多个子空间结构.
- 通过解决波长和时间变异性来增强消噪性能.
主要方法:
- 开发了TenMSR以在多个张量子空间中表示RSI数据.
- 引入了基于非线性转换的3D张量核规范,用于低级别的表征.
- 实现了一个使用近接交替最小化 (PAM) 框架进行模型优化的算法.
主要成果:
- 十MSR准确地描述了RSIs中的波长差异和时间变化.
- 该方法在张量多子空间内实现了更紧的图像分布.
- 实验结果表明,与最先进的单个子空间方法相比,性能优越.
结论:
- 通过利用多个子空间特征,TenMSR有效地消除了RSI的混合噪声.
- 拟议的方法比传统的单个子空间消噪技术有了显著的进步.
- TenMSR为处理复杂的遥感数据提供了一个强大的框架.
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