# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Datasets
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{6}{c}{**(a)**} \\ \hline \multicolumn{6}{c}{**Salinas**} \\ \hline \multicolumn{6}{c}{**Spp**} \\ \hline \multicolumn{6}{c}{**Pixels**} \\ \hline \# & **Classes** & **Train** & **Test** & **Train** & **Test** \\ \hline \(1\) & \multicolumn{1}{c}{Brocoli\_gw\_1} & \(13\) & \(1\) & \(11\) & \(1575\) \\ \(2\) & \multicolumn{1}{c}{Brocoli\_gw\_2} & \(20\) & \(9\) & \(15\) & \(2971\) \\ \(3\) & \multicolumn{1}{c}{Fallow} & \(26\) & \(13\) & \(11\) & \(1573\) \\ \(4\) & \multicolumn{1}{c}{Fallow\_r\_p} & \(17\) & \(9\) & \(9\) & \(1120\) \\ \(5\) & \multicolumn{1}{c}{Fallow\_smooth} & \(37\) & \(16\) & \(15\) & \(2117\) \\ \(6\) & \multicolumn{1}{c}{Stubble} & \(57\) & \(14\) & \(22\) & \(3137\) \\ \(7\) & \multicolumn{1}{c}{Celery} & \(26\) & \(6\) & \(14\) & \(2867\) \\ \(8\) & \multicolumn{1}{c}{Grapes\_unt} & \(116\) & \(41\) & \(58\) & \(8952\) \\ \(9\) & \multicolumn{1}{c}{Soil\_v\_d} & \(74\) & \(20\) & \(26\) & \(4915\) \\ \(10\) & \multicolumn{1}{c}{Corn\_s\_g\_w} & \(58\) & \(12\) & \(24\) & \(2615\) \\ \(11\) & \multicolumn{1}{c}{Lettuc\_4 wk} & \(15\) & \(10\) & \(8\) & \(842\) \\ \(12\) & \multicolumn{1}{c}{Lettuc\_5 wk} & \(28\) & \(11\) & \(15\) & \(1528\) \\ \(13\) & \multicolumn{1}{c}{[PERSON]\_6 wk} & \(15\) & \(7\) & \(6\) & \(726\) \\ \(14\) & \multicolumn{1}{c}{[PERSON]\_7 wk} & \(21\) & \(3\) & \(6\) & \(848\) \\ \(11\) & \multicolumn{1}{c}{Vinyard\_unt} & \(77\) & \(26\) & \(38\) & \(5781\) \\ \(16\) & \multicolumn{1}{c}{Vinyard\_v\_t} & \(16\) & \(2\) & \(10\) & \(1426\) \\ \hline \multicolumn{6}{c}{**(b)**} \\ \hline \multicolumn{6}{c}{**PaviaU**} \\ \hline \multicolumn{6}{c}{**Spp**} \\ \hline \multicolumn{6}{c}{**Pixels**} \\ \hline \# & **Classes** & **Train** & **Test** & **Train** & **Test** & **Classes** & **Train** & **Test** \\ \hline \(1\) & \multicolumn{1}{c}{Asphalt} & \(87\) & \(29\) & \(35\) & \(5207\) & Water & \(833\) & \(302\) & \(340\) & \(52\),\(377\) \\ \(2\) & \multicolumn{1}{c}{Meadows} & \(217\) & \(70\) & \(93\) & \(14\),\(842\) & Trees & \(166\) & \(54\) & \(43\) & \(6020\) \\ \(3\) & \multicolumn{1}{c}{Gravel} & \(23\) & \(7\) & \(9\) & \(1673\) & Asphalt & \(44\) & \(15\) & \(19\) & \(2457\) \\ \(4\) & \multicolumn{1}{c}{Trees} & \(61\) & \(17\) & \(17\) & \(2464\) & S-B Bricks & \(36\) & \(8\) & \(13\) & \(2149\) \\ \(5\) & \multicolumn{1}{c}{Metal} & \(21\) & \(4\) & \(8\) & \(1079\) & Bitumen & \(89\) & \(20\) & \(39\) & \(5263\) \\ \(6\) & \multicolumn{1}{c}{Bare soil} & \(42\) & \(19\) & \(26\) & \(4004\) & Tiles & \(188\) & \(55\) & \(48\) & \(7328\) \\ \(7\) & \multicolumn{1}{c}{Bitumen} & \(16\) & \(4\) & \(4\) & \(1065\) & Shadows & \(58\) & \(22\) & \(34\) & \(5821\) \\ \(8\) & \multicolumn{1}{c}{Bricks} & \(56\) & \(16\) & \(29\) & \(2888\) & Meadows & \(516\) & \(171\) & \(218\) & \(34\),\(057\) \\ \(9\) & \multicolumn{1}{c}{Shadows} & \(21\) & \(6\) & \(3\) & \(754\) & Bare Soil & \(86\) & \(25\) & \(14\) & \(2280\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Salinas, PaviaU and PaviaC scenes. Color codes for the classes and number of samples in the training and testing sets used for both superpixel and pixel-based classification.