Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hyperspectral (HRHS) image. Existing Bayesian-based HS pansharpening methods require designing handcraft image prior to characterize the image features, and deep learning-based HS pansharpening methods usually require a large number of paired training data and suffer from poor generalization ability. To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods. Specifically, we assume that the HRHS image can be recovered from the product of two low-rank tensors, i.e., the base tensor and the coefficient matrix. The base tensor lies on the image field and has a low spectral dimension. Thus, we can conveniently utilize a pre-trained remote sensing diffusion model to capture its image structures. Additionally, we derive a simple yet quite effective way to pre-estimate the coefficient matrix from the observed LRHS image, which preserves the spectral information of the HRHS. Experimental results demonstrate that the proposed method performs better than some popular traditional approaches and gains better generalization ability than some DL-based methods. The code is released in .

中文翻译：

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通过低阶扩散模型进行无监督高光谱全色锐化

高光谱全色锐化是将高分辨率全色 (PAN) 图像和低分辨率高光谱 (LRHS) 图像合并以创建单个高分辨率高光谱 (HRHS) 图像的过程。现有的基于贝叶斯的 HS 全色锐化方法需要在表征图像特征之前设计手工图像，而基于深度学习的 HS 全色锐化方法通常需要大量配对训练数据，泛化能力较差。为了解决这些问题，在这项工作中，我们通过同时利用预训练深度扩散模型的强大功能和贝叶斯方法更好的泛化能力，提出了一种用于高光谱全色锐化的低阶扩散模型。具体来说，我们假设 HRHS 图像可以从两个低秩张量（即基础张量和系数矩阵）的乘积中恢复。基础张量位于像场上并且具有较低的谱维数。因此，我们可以方便地利用预先训练的遥感扩散模型来捕获其图像结构。此外，我们推导了一种简单但非常有效的方法来从观察到的 LRHS 图像中预先估计系数矩阵，该方法保留了 HRHS 的光谱信息。实验结果表明，所提出的方法比一些流行的传统方法表现更好，并且比一些基于深度学习的方法获得更好的泛化能力。代码发布于 .