# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Superpixel-Based Classification
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**(a)**} \\ \hline \multicolumn{5}{c}{**Salinas**} \\ \hline
**Technique** & **OA** & **AA** & \(\kappa\) \\ \hline None & 93.86 \(\pm\) 1.33 & 95.71 & 93.16 \\ Rotate 4\(\times\) & 94.02 \(\pm\) 2.30 & 95.53 & 93.33 \\ Inner-Rotate 4\(\times\) * & 95.18 \(\pm\) 0.89 & 95.71 & 94.63 \\ Dual-Rotate 4\(\times\) * & 86.24 \(\pm\) 3.23 & 89.83 & 84.64 \\ Dual-Rotate 16\(\times\) * & **97.04 \(\pm\) 0.35** & 97.45 & 96.71 \\ Flip 4\(\times\) & 95.02 \(\pm\) 1.05 & 94.34 & 94.46 \\ Inner-Flip 4\(\times\) * & 95.66 \(\pm\) 0.63 & 95.95 & 95.17 \\ Dual-Flip 4\(\times\) * & 87.30 \(\pm\) 2.00 & 91.05 & 85.80 \\ Dual-Flip 16\(\times\) * & 96.55 \(\pm\) 0.44 & 96.36 & 96.15 \\ PVS(+/ \(-\)) 4\(\times\) [12] & 95.22 \(\pm\) 0.46 & 96.30 & 94.67 \\ PVS(+/ \(-\)) 16\(\times\) [12] & 95.97 \(\pm\) 0.37 & 95.96 & 95.51 \\ Random Occlusion 4\(\times\) [13] & 95.22 \(\pm\) 0.50 & 95.24 & 94.68 \\ Random Occlusion 16\(\times\) [13] & 95.85 \(\pm\) 0.42 & 96.59 & 95.38 \\ \hline \hline \multicolumn{5}{c}{**(b)**} \\ \hline \multicolumn{5}{c}{**PaviaU**} \\ \hline
**Technique** & **OA** & **AA** & \(\kappa\) & **OA** & **AA** & **\(\kappa\)** \\ \hline None & 89.88 \(\pm\) 0.88 & 87.06 & 86.50 & 96.76 \(\pm\) 0.70 & 90.00 & 95.40 \\ Rotate 4\(\times\) & 93.02 \(\pm\) 0.63 & 89.10 & 90.71 & **97.70 \(\pm\) 0.18** & 93.17 & 96.74 \\ Inner-Rotate 4\(\times\) * & 91.46 \(\pm\) 1.05 & 88.28 & 88.67 & 97.33 \(\pm\) 0.20 & 92.07 & 96.22 \\ Dual-Rotate 4\(\times\) * & 88.90 \(\pm\) 1.27 & 81.14 & 85.20 & 95.84 \(\pm\) 0.84 & 87.57 & 94.11 \\ Dual-Rotate 16\(\times\) * & 91.99 \(\pm\) 0.56 & 88.02 & 89.39 & 97.49 \(\pm\) 0.20 & 93.11 & 96.44 \\ Flip 4\(\times\) & 93.69 \(\pm\) **1.02** & 90.81 & 91.62 & 97.60 \(\pm\) 0.23 & 92.80 & 96.60 \\ Inner-Flip 4\(\times\) * & 92.33 \(\pm\) 0.49 & 87.86 & 89.80 & 97.12 \(\pm\) 0.24 & 91.21 & 95.92 \\ Dual-Flip 4\(\times\) * & 88.50 \(\pm\) 1.26 & 81.89 & 84.52 & 96.15 \(\pm\) 0.54 & 88.39 & 94.54 \\ Dual-Flip 16\(\times\) * & 91.00 \(\pm\) 0.63 & 84.26 & 88.00 & 97.58 \(\pm\) 0.18 & 92.96 & 96.57 \\ PVS(+/ \(-\)) 4\(\times\) [12] & 89.82 \(\pm\) 0.44 & 84.63 & 86.42 & 97.11 \(\pm\) 0.20 & 91.08 & 95.90 \\ PVS(+/ \(-\)) 16\(\times\) [12] & 90.68 \(\pm\) 0.74 & 86.41 & 87.57 & 97.36 \(\pm\) 0.16 & 91.50 & 96.25 \\ Random Occlusion 4\(\times\) [13] & 89.45 \(\pm\) 0.58 & 84.61 & 85.96 & 97.07 \(\pm\) 0.24 & 90.11 & 95.85 \\ Random Occlusion 16\(\times\) [13] & 91.08 \(\pm\) 0.48 & 87.94 & 88.12 & 97.15 \(\pm\) 0.19 & 91.12 & 95.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Superpixel-based classification performance (in percent) for Salinas, PaviaU and PaviaC scenes using the training and testing sets detailed in Table 2. The best result for each scene is displayed in bold. Figure 14: Oitaven. Evolution of loss (**left**) and accuracy (**right**) with Dual-Flip 16\(\times\) data augmentation. work achieved the best results: dual-rotate 16\(\times\) and dual-flip 16\(\times\) reached 96.20% and 96.19% OA for Oitaven, respectively. The results for Ermidas, Eiras and Mestas share many similarities, with dual-flip 16\(\times\) and dual-rotate 16\(\times\) leading in terms of OA. When a lower data augmentation factor is considered (4\(\times\)), inner-flip 4\(\times\) and inner-rotate 4\(\times\) can be seen systematically obtaining higher OA values than other methods. It can be seen that techniques based on flips tend to perform better than those based on rotations when applied under similar constraints. Figure 15 shows the resulting classification maps for the images of the dataset.