# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 4 Results
### Results on the Validation Sites  
In the initial baseline experiments, we employed a U-Net model that solely utilized the concatenated amplitude information from the two acquisitions as input. However, as indicated in the first row of Table 5, the results demonstrated low F1-score values. It became evident that the model struggled to classify the changes accurately. Indeed, as shown in Figures 4-7, the baseline model introduced false positives in areas where we did not have an effective change, despite the low coherence values associated with them. Indeed, areas characterized by low coherence values do not necessarily contain a change of interest. It is also possible to highlight that the model trained with site B, despite a low number of training patches, i.e., 188, generalized better on the validation set with respect to the other configuration (Site A used for training). This behavior is caused by the fact that mining area land use types are highly complex and heterogeneous. Mining sites frequently have different amplitude distributions, due to the diverse land cover types within a single scene. This heterogeneity, which complicates the detection and distinction of changes within open-pit mines, demonstrates the need for training a model for this specific task. We conducted subsequent experiments to investigate the potential benefits of incorporating the coherence layer as an additional feature. Including coherence information in the network yielded performance improvements, as illustrated in Table 5. The coherence proved to be particularly valuable in both the two-fold cross-validation setups. This outcome was significant, considering that Site A and Site B exhibited distinct amplitude distributions among their respective patches. Consequently, the coherence played a crucial role in bridging the gap between these diverse amplitude distributions. In addition, as shown in Figures 4-7, the coherence helped the network to reduce false positive and better delineate the edges of the change areas. Building upon these findings, the subsequent experiments were designed to utilize the following fixed inputs: the amplitude of both images, and the coherence between them. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Parameters Configuration**} \\ \hline  & \(\mathbf{A^{Train}-B^{Val}}\) & \(\mathbf{B^{Train}-A^{Val}}\) \\ \hline Optimizer & AdamW [(40)] & AdamW [(40)] \\ Base learning rate & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) \\ Weight decay & 0.01 & 0.01 \\ Amsgrad & False & False \\ Momentum & \(\beta_{1},\beta_{2}=0.9\), 0.999 & \(\beta_{1},\beta_{2}=0.9\), 0.999 \\ Batch size & 64 & 64 \\ Training epochs & 300 & 400 \\ Scheduler step & 270 & 370 \\ Scheduler value & 0.1 & 0.1 \\ Precision & Mixed precision (16 bit) & Mixed precision (16 bit) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training optimizer parameters used for validation. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**F1-Score**} \\ \hline
**Configuration** & \(\mathbf{A^{Train}-B^{Val}}\) & \(\mathbf{B^{Train}-A^{Val}}\) \\ \hline
**Pseudo-Label Set** & \(\mathbf{B}\) & \(\mathbf{A}\) \\ \hline Baseline & \(0.4515\pm 0.0288\) & \(0.5645\pm 0.0270\) \\ + Coherence & \(0.6825\pm 0.0163\) & \(0.6375\pm 0.0165\) \\ + Augmentations & \(0.7064\pm 0.0207\) & \(0.7730\pm 0.0096\) \\ + Pseudo Labeling & \(\mathbf{0.7459}\pm 0.0111\) & \(\mathbf{0.7938}\pm 0.0206\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation results. We the report mean \(\pm\) std of three runs. Figure 4: Comparison of the methods on a validation patch extracted from Site A.  
Figure 5: Comparison of the methods on a validation patch extracted from Site A.  
Figure 6: Comparison of the methods on a validation patch extracted from Site B. The limited size of our dataset presented challenges in terms of model training instability and the potential for overfitting. To mitigate these issues, we implemented various data augmentation techniques during the training process.