# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 4 Experimental Results and Discussion
### Impact of the Dataset Augmentation  
In general, the model that fine-tunes the entire network (such as updating all the weights) is prone to suffer the overfitting problem, especially among the first few layers when the new dataset is not large enough [32]. Dataset augmentation is a simple but effective technique to reduce the effect of overfitting and to improve performance. While pretrained CNNs can be applied to reduce the effect of the problem of a limited dataset, dataset augmentation still has the potential to improve classification performance, which was verified by [PERSON] et al., 2015 [31]. It is common that the more training dataset CNNs have, the better results could be obtained. [PERSON] et al., 2014 [33] pointed out that fine-tuning a network pretrained with the ImageNet dataset has a positive effect on a target task, such as image classification and object detection, and this effect increases when more data is used for fine-tuning. Thus, there is still potential to improve the classification accuracy by increasing the number of training dataset, and dataset augmentation can be applied to reduce the effect of overfitting problem in the fine-tuning procedure.  
Figure 4 shows the loss curves and consumed time when dataset augmentation was considered. The VGGNet model was using pre-trained weights rather than random weights. The loss curve can be used for supervised learning procedure, if the loss is decreasing, it indicates that the network is learning effective features. Fine-tuned CNNs has an advantage of the speed of convergence. The loss decreased sharply along with the number of increasing epochs at the beginning for all dataset augmentation size. For the 1\(\times\) and 2\(\times\) dataset augmentation (\(\times\) represents the multiple of the data increased by dataset augmentation), the loss values significantly increased after 60 epochs which implies that overfitting significantly affects the performance of the fine-tuned pretrained VGGNet model, and slight overfitting took place for 4\(\times\) and 8\(\times\) dataset augmentation. After 160 epochs, both loss curves for 16\(\times\) and 32\(\times\) became flat and vibrates in a small range, which indicated that it is hard to obtain more improvement with further training based on the current parameter complexity. Figure 4B shows that the training time increased with the increase of training data. It was noteworthy that the model with 32\(\times\) obtained the lowest loss value, however, it needs about twice as much time to perform the model compared to the model with 16\(\times\). Time is precious for planning a rescue after an earthquake. The experiment demonstrated the 16\(\times\) method achieved relatively fast training and high accuracy. Therefore, 16\(\times\) was chosen as the dataset augmentation coefficient.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**OA 1 (\%)**} & \multirow{2}{*}{**Kappa (\%)**} & \multicolumn{2}{c}{**Collapsed**} & \multicolumn{2}{c}{**Non-Collapsed**} \\ \cline{4-7}  & & & **PA 2 (\%)** & **UA 3 (\%)** & **PA (\%)** & **UA (\%)** \\ \hline Pretrained VGGNet & 85.19 & 67.14 & 81.70 & 75.29 & 86.90 & 90.67 \\ VGGNet & 83.38 & 60.69 & 59.21 & 90.96 & 96.75 & 81.09 \\ \hline \hline \end{tabular}  
* Overall accuracy; 2 Producer accuracy; 3 User accuracy.  
\end{table}
Table 1: Performance of identification of collapsed buildings using Visual Geometry Group (VGG)Net model and fine-tuned VGGNet model.