# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 5 Results and Analysis
### Hyperspectral Unmixing
As shown in Table 3, the ERGAS produced by our method was 4.92% less than the second-best performed baseline and 68.85% less than the worst-performing baseline. Figure 9: Visual super-resolution results comparison for imagel in the Harvard data set. It should be noted that, in Table 3, the number before the symbol \"\(\pm\)\" is the average value and the number after the symbol \"\(\pm\)\" is the standard deviation. The standard deviation indicates how the quality metric values differ from the average value. We can observe that our method obtained the best average values and the smallest standard deviations (except for ERGAS), which means our method not only performed better but was also more stable than the other compared methods. Similarly, in Figure 10, the estimated spectral responses and the ground truth for the \"UNIVERSITY OF PAVIA\" data set are shown. The pseudo-color image of the super-resolution HSI is presented on the left side. It was visually well-reconstructed, compared to the input conventional image, as shown in Figure 2d. Our method also obtained a satisfactory result, in terms of spectrum reconstruction. As shown in the reflectance curves on the right side, the spectra of three randomly selected pixel points were well-reconstructed. As an example of visual results for the remote sensing data sets considered, Figure 11 shows a visual comparison of the results of all the considered methods for the \"UNIVERSITY OF PAVIA\" data set. The CSU results show an obvious difference from the ground truth. A careful check of the building in the red square reveals that the error maps of FUSE, BSR, and CSU show an obvious shadow of the building. The error maps of CMS, LTTR, and Ours were closer to the ground truth. The bottom three error maps were very close to each other, but we can still see that our method achieved a smaller reconstruction error, indicating that the proposed method could reconstruct detailed spatial structures more accurately. Considering the visual deviation of the human eye, a quantitative comparison for remote sensing data sets is presented in the following discussion. Similar to the \"Harvard\" data set, Table 4 shows the results for all of the remote sensing data sets. The best values are highlighted in bold. In addition, if our method did not achieve the best result in a certain metric, but the second-best, it is highlighted and underlined. All quality metrics of the proposed method on the \"CUPRITE\" data set outperformed the other methods. For the \"CHIKUSEI\" data set, the values of PSNR and \(Q2^{n}\) for the proposed method were the second-best in all methods, while SSIM, RMSE, SAM, and ERGAS were all the best. For the \"INDIAN PINES\" data set, our method obtained the best SSIM, PSNR, SAM, and ERGAS, and the second-best RMSE. For the \"UNIVERSITY OF PAVIA\" data set, our method obtained the best SSIM, RMSE, and SAM, and the second-best \(Q2^{n}\). For the \"UNIVERSITY OF HOUSTON\" data set, all quality metrics were superior, except for RMSE (which was the second-best). Figure 10: Super-resolution results for the UNIVERSITY OF PAVIA data set. Figure 11: Visual super-resolution results comparison for the UNIVERSITY OF PAVIA data set.