# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Superpixel-Based Classification  
This section contains the experimental results of the proposal for superpixel-based classification, as described in Section 2. A single scenario training the network with the 60% of the labeled superpixels available has been considered. The classification performance was measured at the pixel level, i.e., considering the same label for all the pixels in the same superpixel. In order to select values for superpixel size and patch size, some tests were run using one image considered as representative of each of the datasets. PaviaC was selected from the standard dataset and Oitaven was chosen as the counterpart from the Galicia dataset. The results were obtained for two scenarios, one where no augmentation was applied, and a second one where the DWS-based dual-flip 16\(\times\) technique was used in order to improve accuracy. Values for superpixel sizes each represent an average superpixel area used in the SLIC segmentation. Values for patch sizes each represent the size of a side of the square patch used in the CNN classification process. Inner region sizes are 10 pixels smaller than the corresponding outer regions. The relationship between inner and outer size was chosen as a trade-off between the variability introduced by the transformations applied to the data and the relevance of the inner region. Small changes in inner patch size would not significantly alter the results obtained. Figures 9 and 10 show the overall classification accuracy for the PaviaC and Oitaven images, respectively, as the superpixel area (left) and the patch size (right) vary. In general, we can observe an inverse correlation between superpixel sizes and accuracy of the CNN model. The accuracy without applying data augmentation is correlated with patch size, and bigger patches produce better results for the Oitaven image. The observed effect is smaller in the case of PaviaC. In this last case, the oscillations in the graph are caused by the dispersion of the values across experiments. Finally, results show a very limited increase in accuracy as the size of the patches grows when using the dual-flip augmentation technique. \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Eiras**} & \multicolumn{4}{c}{**Mestas**} \\ \hline  & \multicolumn{4}{c}{**Total**} & \multicolumn{4}{c}{**Total**} & \multicolumn{4}{c}{**Total**} \\ \hline
**\#** & **Classes** & **Spp.** & **Pixels** & **Train** & **Test** & **Classes** & **Spp.** & **Pixels** & **Train** & **Test** \\ \hline
1 & Water & 1379 & 734,617 & 823 & 278 & Water & - & - & - & - \\
2 & Oak & 5559 & 2,067,380 & 3332 & 1066 & Oak & - & - & - & - \\
3 & Tiles & 17 & 8232 & 12 & 2 & Tiles & - & - & - & - \\
2 & Meadows & 2457 & 773,964 & 1476 & 485 & Meadows & 8903 & 4,489,189 & 5373 & 1780 \\
5 & Asphalt & 145 & 85,209 & 86 & 32 & Asphalt & - & - & - & - \\
6 & Bare Soil & 401 & 96,935 & 247 & 86 & Bare Soil & 2708 & 1,051,275 & 1653 & 487 \\
7 & Rock & 1383 & 144,800 & 830 & 286 & Rock & - & - & - & - \\
8 & Concrete & 85 & 27,061 & 56 & 16 & Concrete & - & - & - & - \\
9 & Endemic veg. & - & - & - & - & Endemic veg. & 214 & 80,858 & 138 & 40 \\
10 & Eucalyptus & 31 & 8451 & 23 & 5 & Eucalyptus & 9329 & 449,5047 & 5540 & 1887 \\
10 & Pines & 309 & 95,132 & 187 & 70 & Pines & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The patch size selected for the experiments was \(25\times 25\) pixels, with an inner region size of \(15\times 15\). The complexity of the proposed CNN is low, and as such, increases in patch size have a small impact during training, allowing us to work with larger amounts of data at this stage. In contrast to this, the amount of samples, most especially when augmentation is applied, has a large impact on the speed of the training stage. Due to this, and in order to keep computation times at moderate values, 50 and 800 were selected as the superpixel sizes for the scenes of the standard and Galicia datasets, respectively.