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CS2DIPs:无监督的HSI超分辨率使用合的空间和光谱DIPs

Yuan Fang, Yipeng Liu, Chong-Yung Chi

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|April 24, 2024

概括

这项研究引入了一种新的合空间和光谱深度图像先验 (CS2DIPs) 方法,用于超光谱图像超分辨率 (HSI-SR). CS2DIPs增强了多光谱和超光谱图像的融合,优于现有的深度图像先前的方法.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
信号处理信号处理

背景情况:

超光谱图像超分辨率 (HSI-SR) 通常将高空间分辨率的多光谱图像 (HR-MSI) 与低空间分辨率的超光谱图像 (LR-HSI) 融合在一起.
深度图像预先 (DIP) 方法在无监督的HSI-SR中很受欢迎,因为没有预先培训的需要,但经常与潜在信息提取作斗争.

研究的目的:

通过改进HR-MSI和LR-HSI的融合,开发一种超光谱图像超分辨率 (HSI-SR) 的先进无监督方法.
通过更好地捕获空间和光谱信息来提高基于DIP的HSI-SR方法的性能.

主要方法:

提出了一种结合空间和光谱深图像先验 (CS2DIPs) 方法,将非负矩阵向量张量分解 (NMVTF) 集成到DIP框架中.
共同学习了丰富度张量和光谱特征矩阵,使用两个合的DIP来捕获HR-MSI和LR-HSI的空间和光谱特征.
使用模式-3张力产物和内置物理约束的引导HSI-SR融合.

主要成果:

在CS2DIPs方法有效地捕获丰富的空间和光谱信息,而不需要培训数据.
与现有的基于DIP的HSI-SR方法相比,显示出明显优异的性能和更快的融合.
广泛的实验证实了与基准方法相比的最先进的整体性能.

结论:

CS2DIPs为HSI-SR提供了一种有效的无监督方法,克服了以前基于DIP的技术的局限性.
该方法对需要高分辨率超光谱数据融合的应用具有很大的潜力.
整合NMVTF和合的DIP为HSI-SR提供了一个强大的框架.

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