# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 5 Results and Analysis
### Impact on Classification  
HSI classification is one of the most relevant topics in HSI processing. The quality of the fused images can be indirectly validated by pixel-wise classification. The classification performance was quantitatively validated using the overall accuracy (OA). The classification method we used is based on the K-Nearest Neighbors (KNN) classifier [42]. As the absolute OA value for classification is not  
\begin{table} \begin{tabular}{c c c c c c c} \hline \hline  & **Methods** & **FUSE** & **BSR** & **CSU** & **CMS** & **LTTR** & **Ours** \\ \hline \multirow{6}{*}{CUPRITE} & SSIM & 0.83747 & 0.98963 & 0.99192 & 0.97348 & 0.95545 & **0.99238** \\ \cline{2-7}  & RMSE & 4.9375 & 1.15290 & 1.0562 & 2.08420 & 2.15130 & **0.98903** \\ \cline{2-7}  & PSNR & 31.6723 & 41.1775 & 41.8221 & 37.0663 & 40.8065 & **42.6838** \\ \cline{2-7}  & SAM & 3.635 & 0.76047 & 0.65195 & 0.88845 & 0.93718 & **0.62051** \\ \cline{2-7}  & ERGAS & 1.2788 & 0.3173 & 0.29971 & 0.54418 & 0.55328 & **0.26375** \\ \cline{2-7}  & \(Q^{2n}\) & 0.80201 & 0.97817 & 0.97233 & 0.89626 & 0.97452 & **0.97993** \\ \cline{2-7}  & Time (s) & 1.29 & 2022.60 & 111.01 & 206.09 & 340.67 & 131.65 \\ \hline \multirow{6}{*}{CHIKUSEI} & SSIM & 0.99651 & 0.99575 & 0.99603 & 0.98038 & 0.99215 & **0.99706** \\ \cline{2-7}  & RMSE & 1.1972 & 1.2076 & 1.1823 & 2.9822 & 1.7675 & **1.0371** \\ \cline{2-7}  & PSNR & **45.4159** & 43.9835 & 42.4042 & 38.7097 & 42.6187 & **44.4753** \\ \cline{2-7}  & SAM & 1.4699 & 1.5546 & 1.3871 & 2.2058 & 2.0718 & **1.2426** \\ \cline{2-7}  & ERGAS & 1.6222 & 1.8631 & 1.7196 & 1.7487 & 1.6666 & **1.6081** \\ \cline{2-7}  & \(Q^{2n}\) & 0.91975 & **0.94963** & 0.91864 & 0.86428 & 0.87594 & 0.92592 \\ \cline{2-7}  & Time (s) & 2.91 & 2235.74 & 233.58 & 203.61 & 720.26 & 265.67 \\ \hline \multirow{6}{*}{INDIAN} & SSIM & 0.90796 & 0.98916 & 0.98807 & 0.97736 & 0.97826 & **0.99075** \\ \cline{2-7}  & RMSE & 9.5287 & **1.3632** & 1.5157 & 2.2509 & 1.6519 & 1.3892 \\ \cline{2-7}  & PSNR & 35.5488 & 40.4208 & 41.3034 & 40.1867 & 41.0773 & **42.9987** \\ \cline{2-7}  & SAM & 6.4675 & 0.79846 & 0.88154 & 0.96104 & 0.83385 & **0.82111** \\ \cline{2-7}  & ERGAS & 1.7171 & 0.4197 & 0.41053 & 0.55839 & 0.58602 & **0.33396** \\ \cline{2-7}  & \(Q^{2n}\) & 0.38834 & 0.76825 & 0.70134 & 0.81233 & **0.88700** & 0.71257 \\ \cline{2-7}  & Time (s) & 2.80 & 1924.67 & 157.41 & 280.83 & 570.40 & 173.08 \\ \hline \multirow{6}{*}{UNIVERSITY} & SSIM & 0.9805 & 0.98122 & 0.98029 & 0.94498 & 0.93797 & **0.98206** \\ \cline{2-7}  & RMSE & 2.0836 & 2.1192 & 2.3904 & 4.3442 & 4.5258 & **2.0834** \\ \cline{2-7}  & PSNR & **41.6695** & 41.6288 & 39.5331 & 35.8998 & 35.9253 & 41.0519 \\ \cline{2-7}  & SAM & 2.8931 & 3.0125 & 2.7325 & 4.3366 & 5.1832 & **2.6367** \\ \cline{2-7}  & ERGAS & **0.83523** & 0.86948 & 0.97139 & 1.5170 & 1.8383 & 0.8492 \\ \cline{2-7}  & \(Q^{2n}\) & **0.88702** & 0.87856 & 0.80358 & 0.62222 & 0.59185 & 0.84299 \\ \cline{2-7}  & Time (s) & 1.98 & 1648.0 & 175.0 & 210.96 & 424.48 & 203.73 \\ \hline \multirow{6}{*}{UNIVERSITY} & SSIM & 0.95808 & 0.95452 & 0.96435 & 0.76379 & 0.64306 & **0.96456** \\ \cline{2-7}  & RMSE & 7.6814 & 8.5580 & **7.3756** & 20.9834 & 24.4054 & 7.3900 \\ \cline{2-7}  & PSNR & 32.155 & 31.574 & 32.7052 & 23.8961 & 28.877 & **32.7105** \\ \cline{2-7}  & SAM & 2.2091 & 2.2500 & 2.0754 & 4.7053 & 6.2191 & **2.0917** \\ \cline{2-7}  & ERGAS & 3.4876 & 3.9151 & 3.255 & 8.2063 & 5.1517 & **3.2605** \\ \cline{2-7}  & \(Q^{2n}\) & 0.90252 & 0.88463 & 0.90889 & 0.69074 & 0.65189 & **0.91488** \\ \cline{2-7}  & Time (s) & 1.47 & 1904.26 & 145.68 & 93.79 & 638.94 & 114.72 \\ \hline \hline \end{tabular}  \endmainly concerned in this paper, we set the parameter k as 5 and the ratio of training data as 0.1 for lower processing time. This led to not very high OA results, but this did not affect the verification of our super-resolution method. The \"UNIVERSITY OF PAVIA\" data set has also been widely used in HSI classification, due to the availability of ground truth data.