# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Pixel-Based Classification
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Eiras**} & \multicolumn{3}{c}{**Mestas**} \\ \hline
**Technique** & **OA** & **AA** & **\(\kappa\)** & **OA** & **AA** & **\(\kappa\)** \\ \hline None & 95.92 \(\pm\) 0.53 & 88.22 & 93.85 & 91.05 \(\pm\) 0.21 & 85.23 & 84.85 \\ Rotate 4\(\times\) & 96.82 \(\pm\) 0.32 & 90.99 & 95.21 & 90.76 \(\pm\) 0.41 & 84.24 & 84.32 \\ Inner-Rotate 4\(\times\) * & 96.82 \(\pm\) 0.35 & 88.19 & 95.21 & 91.32 \(\pm\) 0.18 & 86.07 & 85.30 \\ Dual-Rotate 4\(\times\) * & 96.36 \(\pm\) 0.19 & 85.63 & 94.51 & 91.06 \(\pm\) 0.13 & 84.37 & 84.86 \\ Dual-Rotate 16\(\times\) * & 97.08 \(\pm\) 0.25 & 88.37 & 95.60 & 91.67 \(\pm\) 0.10 & 87.65 & 85.93 \\ Flip 4\(\times\) & 96.92 \(\pm\) 0.24 & 89.47 & 95.36 & 90.75 \(\pm\) 0.34 & 83.62 & 84.30 \\ Inner-Flip 4\(\times\) * & 96.96 \(\pm\) 0.17 & 89.65 & 95.42 & 91.27 \(\pm\) 0.24 & 86.91 & 85.24 \\ Dual-Flip 4\(\times\) * & 96.79 \(\pm\) 0.22 & 87.15 & 95.16 & 91.15 \(\pm\) 0.21 & 85.69 & 85.02 \\ Dual-Flip 16\(\times\) * & 97.13 \(\pm\)**0.20** & 88.81 & 95.67 & **91.70 \(\pm\)**0.09** & 87.51 & 85.97 \\ PVSA(+/\(-\)) 4\(\times\)[12] & 96.35 \(\pm\) 0.26 & 85.70 & 94.49 & 91.31 \(\pm\) 0.24 & 86.89 & 85.30 \\ PVSA(+/\(-\)) 16\(\times\)[12] & 96.36 \(\pm\) 0.26 & 86.27 & 94.49 & 91.35 \(\pm\) 0.14 & 85.32 & 85.34 \\ Random Occlusion 4\(\times\)[13] & 95.79 \(\pm\) 0.28 & 85.13 & 93.64 & 90.72 \(\pm\) 0.19 & 82.66 & 84.27 \\ Random Occlusion 16\(\times\)[13] & 96.83 \(\pm\) 0.20 & 86.15 & 95.21 & 91.47 \(\pm\) 0.11 & 86.24 & 85.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Table 8 summarizes the pixel-based classification results for the Salinas, PaviaU and PaviaC images. It can be seen that dual-flip 16\(\times\) obtained the highest OA for the Salinas scene; inner-flip 4\(\times\) yielded the highest OA for PaviaU; and flip 4\(\times\) was the best performing technique for PaviaC. Methods based on flip operations consistently outperformed rotations in this scenario. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Superpixel-Based**} & \multicolumn{4}{c}{**Pixel-Based**} \\ \hline
**Method** & **Training** & **Prediction** & **Total** & **Training** & **Prediction** & **Total** \\ \hline None & 76.48 & 5.09 & 81.57 & 31.26 & 2453.79 & 2485.05 \\ Rotate 4\(\times\) & 339.76 & 6.01 & 345.77 & 137.19 & 2645.13 & 2782.32 \\ Inner-Rotate 4\(\times\) * & 325.28 & 5.88 & 331.16 & 130.44 & 2611.15 & 2741.59 \\ Dual-Rotate 4\(\times\) * & 328.10 & 6.54 & 334.64 & 135.38 & 2550.93 & 2686.31 \\ Dual-Rotate 16\(\times\) * & 1305.47 & 6.16 & 1311.63 & 508.97 & 2770.77 & 3279.74 \\ Flip 4\(\times\) & 325.65 & 5.30 & 330.95 & 129.62 & 2555.46 & 2655.08 \\ Inner-Flip 4\(\times\) * & 327.90 & 5.35 & 333.25 & 132.81 & 2541.49 & 2674.30 \\ Dual-Flip 4\(\times\) * & 318.98 & 6.01 & 324.99 & 132.51 & 2528.27 & 2660.78 \\ Dual-Flip 16\(\times\) * & 1320.23 & 7.47 & 1327.70 & 505.77 & 2739.75 & 3245.52 \\ PVSA(+/-) 4\(\times\) [12] & 329.50 & 7.62 & 337.12 & 133.04 & 2767.88 & 2900.92 \\ PVSA(+/-) 16\(\times\) [12] & 1306.37 & 7.53 & 1313.90 & 509.75 & 2770.44 & 3280.19 \\ Random Occlusion 4\(\times\) [13] & 325.10 & 7.98 & 333.08 & 127.09 & 2724.73 & 2851.82 \\ Random Occlusion 16\(\times\) [13] & 1297.85 & 7.58 & 1305.43 & 510.65 & 2752.50 & 3263.15 \\ \hline \hline \end{tabular}  
\end{table} 
Table 7: Execution times (in seconds) for the classification of the PaviaC scene and for each augmentation technique. Prediction time refers to the computation for the whole image.