# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 2 Dataset
For example, in urban planning activities, changes related to weather conditions (e.g., rain, snow) may be irrelevant; meanwhile, they could be important in agriculture applications. In our case, we only want to highlight changes in reflectivity linked to modifications in the areas due to events like waste pile operations, blasting, or collapsing. On the contrary, we do not want to highlight pseudo-change regions in which, although there is a statistically reliable change in the reflectivity, there are no effective changes of interest, e.g., equipment movements or humidity fluctuations. During the hand labeling operation, both the SAR amplitude pairs and the estimation of the coherence [35] between the first and the second acquisition were used by the InSAR specialist to visually detect changes. Figure 1 shows an example of patches extracted from our SAR change detection training dataset. Coherence plays a crucial role in providing valuable insights into the stability and continuity of the observed surface. It quantifies the degree of similarity between two radar images captured of the same area at different times. By comparing the phase information of the radar waves reflected from the surface, coherence measurements can reveal changes in the scattering characteristics of the terrain. The coherence values obtained from radar images can offer valuable information about the temporal behavior of an observed surface. Higher coherence values indicate a high degree of similarity between radar returns, suggesting little to no changes in the surface properties over time. This typically corresponds to areas with stable features such as buildings, roads, or permanent structures. On the other hand, lower coherence values signify significant changes in the observed area. These changes can arise from various factors such as alterations in the surface morphology, vegetation growth or removal, changes in water bodies, or the presence of moving objects. In such cases, the coherence measurements capture the temporal discrepancies in the radar wave interactions, reflecting the dynamic nature of the surface and highlighting areas that have undergone changes between the acquisition time points. However, it is essential to underline that in our specific task, areas of low coherence do not always indicate a change of interest. Indeed, for example, areas relating to pseudo changes, even though they may have low coherence values, must not be highlighted as a change. By incorporating coherence information into our change detection methodology, as shown in the Section 4, we gained a more comprehensive understanding of the changes occurring in the observed environment, allowing for a more robust and accurate open-pit mine change detection model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Size** & **Pairs** & **Number of Patches** & **Number of Training Patches** \\ \hline A & \(5352\times 9356\) & 3 & 2160 & 828 \\ B & \(5099\times 4939\) & 2 & 722 & 188 \\ C & \(6040\times 6312\) & 4 & 2208 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the labeled open-pit mining sites used to train our model.