多光谱图像光谱分辨率高，但空间分辨率低，因此严重制约了其在地物分类、目标识别和异常检测中的后续应用。与全色图像进行融合的Pan-sharpening方法是提高多光谱图像空间分辨率的有效途径。然而，Pan-sharpening方法的主要难点是如何有效兼顾空间几何特征迁移和光谱特征保真，实现高效融合。因此，本项目主要解决全色与多光谱图像Pan-sharpening融合的这一技术难点，以图像张量建模和图像先验建模理论为主线，建立全色与多光谱图像成像退化模型，以及全色与多光谱图像的张量模型与空谱特征张量模型，并研究：1）基于张量的空谱特征联合保持的高阶几何先验建模；2）基于空谱特征张量的空谱结构化低秩先验建模；3）空谱张量结构化低秩与高阶几何先验联合驱动的变分Pan-sharpening模型及其高效并行算法。本项目的研究对于多光谱图像后续分析与应用具有重要的指导意义。

The multispectral image has high spectral resolution, but low spatial resolution, so its applications, such as land feature and mineral classification, object recognition, and anomaly detection, are seriously restricted. Recently, the fusion of multispectral and panchromatic images is an effective approach to improve its spatial resolution. However, the main difficulties of pan-sharpening consist in how to efficiently combine spatial geometric feature transferring and spectral feature preserving. Thus, in this project, we aim to resolve the main difficulties of pan-sharpening. By use of the image tensor modeling and prior modeling theory, we model the imaging degeneration of panchromatic and multispectral images, as well as their tensor models and spatial-spectral feature tensor models, and then propose: 1) the tensor-based spatial-spectral feature jointly preserved higher-order geometry prior models; 2) the spatial-spectral feature tensor-based spatial-spectral structural low-rank prior models; 3) the spatial-spectral tensor structural low-rank and higher-order geometry priors jointly driven variational pan-sharpening models and the efficiently parallel algorithms. The research of this project will have an important guiding significance for the further analysis and applications of multispectral image.