Performance evaluation of pan-sharpening and dictionary learning methods for sparse representation of hyperspectral super-resolution

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.