# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 4 Results
### Results on the Validation Sites
By introducing these augmentations, we aimed to enhance the robustness and generalization capabilities of the model. The results of the trained model, incorporating all the described data augmentation techniques, are presented in Table 5. As shown, the introduction of data augmentations yielded improved F1-scores in both two-fold cross-validation setups. This outcome highlights the effectiveness of the augmentation strategies in enhancing the performance of the model. By augmenting the dataset, we were able to increase the diversity and variability of the training samples, enabling the model to learn more effectively and achieve better overall results. Also in this case, Figures 4-7 show the improvement in the predictions. To further enhance the results achieved using data augmentation techniques, we implemented a pseudo-labeling pipeline that leveraged the network's output as labels for additional training data. The two-fold cross-validation setups were crucial in evaluating the consistency and effectiveness of this proposed methodology. We identified the 200 th epoch as the optimal candidate for generating pseudo-labels for all the experiments conducted. Subsequently, we retrained the network from scratch, employing 100 epochs for the first configuration (\(A^{Train}-B^{Val}\)) and 200 epochs for the second configuration (Site \(B^{Train}-A^{Val}\)). It is important to note that we selected the validation sets as the unlabeled sets in both cases. The decision to use the validation dataset as an unlabeled dataset was mainly due to two reasons. First, we wanted to demonstrate how inserting images, which we validated in training, actually benefited the validation performance. Indeed, as previously explained, different areas can have very different amplitude distributions in SAR images. Therefore, the introduction of new different patches compared to the training patches extracted for a single site helped the model to increase performance. At the same time, the other motivation was related to being able to check the pseudo-labels with respect to the real ground truths. As depicted in Figure 8, the quality of the pseudo-labels generated by the final checkpoint exhibited high accuracy without introducing misclassification points associated with pseudo-change areas. This pseudo-label accuracy subsequently led to a notable performance enhancement when incorporated into the dataset for model retraining. Including the pseudo-labeled data effectively contributed to further refining the model's ability to detect changes, resulting in an improved overall performance, as shown in Table 5. This meant that the approach also allowed for the flexibility of retraining the model using a new mining site as an unlabeled dataset, whenever predictions were required for that particular site. To further assess the model's performance, we visually examined its outputs on selected examples extracted from both the validation sets. As depicted in Figures 4-7, the retrained model exhibited accurate detection of changes within the mining sites. This included the identification of large-scale changes, such as the expansion of the mining pit,  
Figure 7: Comparison of the methods on a validation patch extracted from Site B.  
as well as small-scale changes. Additionally, the model effectively mitigated false positives associated with pseudo changes, correctly identifying stable areas as unchanged. As we will demonstrate in the next section, the introduction of pseudo-labels during the retraining phase enhanced the model's generalization capacity, even when applied to a third site (Site C) that was previously unknown to the model. By leveraging the information contained within the pseudo-labels and incorporating them into the training process, the model became more adept at detecting changes in unseen areas. This capability showcases the robustness and adaptability of our approach, as the model successfully extended its change detection capabilities to a new and unfamiliar site.