【摘要】 低分辨率高光谱图像（LR-HSI）与高分辨率多光谱图像（HR-MSI）融合技术，广泛用于解决图像空间分辨率与光谱分辨率无法同时保持高水平的矛盾。从融合效果上分析，现有算法的空间重建误差与光谱重建误差都主要体现在边缘和细节区域。因此，本文提出了基于细节关注的字典构建和图像重建的融合算法。在光谱特性保持方面，由于图像邻近效应导致在细节区域光谱分布复杂多样，本文提出对图像层和细节层分别进行字典学习。在空间特性增强方面，提出了细节感知误差和边缘方向自适应全变分约束，并将其与局部低秩约束结合在同一个融合框架用来估计稀疏系数。消融实验证明细节感知误差、细节感知字典和EADTV正则项的引入，在Pavia University数据集上分别将整体精度（PNSR）提升了0.0263、0.289和0.4121，光谱（SAM）精度分别提高了0.2%、4.6%和4.3%。在Pavia University数据集和Indian Pine数据集上的对比实验证明，本文算法相对于次优解，PNSR分别提高了0.4945和0.2345，实验结果印证了本文算法有效地提高了融合精度。通过实验对比，本文提出算法的融合结果在空间特性与光谱特性方面较其他算法有明显提升。更多还原

【Abstract】 Hyperspectral image(HSI) and multispectral image(MSI) are two types of images widely used in the field of remote sensing.These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signalto-noise ratio(SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI(LR-HSI) with a high-spatial resolution MSI(HR-MSI) in the same scene to obtain a high-resolution HSI(HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas.The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient.Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics.This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail.更多还原