# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 4 Experiments
### Experimental Setting  
The basic procedure of the experiment was to first obtain the input images based on the ground truth image by simulation and then running various super-resolution algorithms to obtain high spatial resolution HSIs. Finally, the results were compared with the ground truth. The data sets provided an original HSI cube. There is no ground truth in the real world, so the literature typically uses the HSI in the data set as a reference. The input low spatial resolution HSIs and high spatial resolution conventional images were obtained by downsampling, based on these references.  
The source code of all the related works for comparison are open-source. The code for FUSE is available at [[http://wei.perso.enseeiht.fr/publications.html](http://wei.perso.enseeiht.fr/publications.html)]([http://wei.perso.enseeiht.fr/publications.html](http://wei.perso.enseeiht.fr/publications.html)); the code for BSR is available at [[http://openremotesensing.net/wp-content/uploads/2016/12/Supplementary.zip](http://openremotesensing.net/wp-content/uploads/2016/12/Supplementary.zip)]([http://openremotesensing.net/wp-content/uploads/2016/12/Supplementary.zip](http://openremotesensing.net/wp-content/uploads/2016/12/Supplementary.zip)); the code for CSU is available at [[https://github.com/lanha/SupResPALM](https://github.com/lanha/SupResPALM)]([https://github.com/lanha/SupResPALM](https://github.com/lanha/SupResPALM)); the code for CMS is available at [[https://sites.google.com/site/leizhanghyperspectral/publications](https://sites.google.com/site/leizhanghyperspectral/publications)]([https://sites.google.com/site/leizhanghyperspectral/publications](https://sites.google.com/site/leizhanghyperspectral/publications)); and the code for LTTR is available at [[https://github.com/renweidian/LTTR](https://github.com/renweidian/LTTR)]([https://github.com/renweidian/LTTR](https://github.com/renweidian/LTTR)).  
We kept the default parameters for the related works. For the unmixing-based methods, the number of endmembers (\(P\) in Equation (1)) was set to 30, which was sufficient for all data sets. As for our spatial group sparsity regularization-based unmixing, the Lagrange multiplier regularization parameter \(\mu\), the iteration number \(r\), and the error tolerance \(\varepsilon\) were respectively set to \(\mu=10^{-2}\), \(r=1000\), and \(\varepsilon=10^{-6}\). Furthermore, the average width of the superpixels for SLIC was set to 10. The regularization parameter \(\lambda\) plays an important role related to the trade-off between accuracy and sparsity, and was set to \(10^{-2}\).  
For images in the \"Harvard\" data set, all downsampling scale factors were set to 8. The conventional images had three spectral bands, which means that we estimated images with dimensions of \(31\times 1392\times 1040\) from low spatial resolution hyperspectral images with dimensions of \(31\times 174\times 130\) and conventional images with dimensions of \(3\times 1392\times 1040\). For images in the remote sensing data sets, the downsampling scale factors and the numbers of conventional image bands were various, as shown in Table 2.