Because it contains high spectral information, hyperspectral imagery has been used in many areas. However, hyperspectral imagery has low spatial resolution because of imaging hardware limitation. Recently, many methods have been available for improving spatial resolution of hyperspectral images. Pan-sharpening and dictionary learning-based sparse representation methods are well-known methods for improving spatial resolution. In this study, a quantitative analysis of super-resolution methods for hyperspectral imagery is performed for identifying the best method in terms of reconstruction quality and processing time. K-SVD, ODL and Bayesian methods are employed for dictionary learning-based sparse representations. On the other hand, IHS and PCA-based methods are employed for pan-sharpening methods. The experimental results show that the ODL method outperforms others in terms of reconstruction quality measured by RMSE values and processing times.

中文翻译：

---

用于高光谱超分辨率稀疏表示的全色锐化和字典学习方法的性能评估

因为它包含高光谱信息，高光谱图像已被用于许多领域。然而，由于成像硬件的限制，高光谱图像的空间分辨率较低。最近，有许多方法可用于提高高光谱图像的空间分辨率。全色锐化和基于字典学习的稀疏表示方法是众所周知的提高空间分辨率的方法。在这项研究中，对高光谱图像的超分辨率方法进行了定量分析，以确定重建质量和处理时间方面的最佳方法。K-SVD、ODL 和贝叶斯方法用于基于字典学习的稀疏表示。另一方面，全色锐化方法采用基于 IHS 和 PCA 的方法。