# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 5 Discussion  
As already highlighted in [10], the need for a specialized model trained on open-pit mines arises from the complex nature of this domain. Unlike many traditional change detection tasks, open-pit mines present unique challenges. The coexistence of diverse land cover types, coupled with various types of changes within the mining environment, makes this a particularly intricate task. Furthermore, SAR images can be challenging to interpret due to their complex interactions with the ground. Indeed, the backscatter signal may vary significantly based on surface roughness, incidence angle, and temporal differences [41]. To address these challenges, our study focused on developing a deep learning model specifically tailored to open-pit mine change detection. We acknowledged the limitations of traditional signal processing methods [30] and, by doing so, aimed to achieve more effective and accurate change detection in this sector. The low F1-score values in Table 5, obtained when employing only the amplitude information, demonstrate that the model could not generalize on different land cover distributions and only classified areas linked to actual changes related to mining operations. Thus, SAR amplitude data alone may not capture specific, subtle changes effectively or distinguish slight variations. The pivotal roles of data augmentations and the coherence layer as indispensable elements were confirmed by the significant improvements in the F1-score values. As shown in Table 5, coherence proved to be additional information fundamental for eliminating false positives related to pseudo changes and for addressing the lack of data, which does not allow the model to generalize from different amplitude distributions. It is essential to highlight that our method is the first in the literature to employ coherence with amplitude in a change detection task, underscoring the potential of InSAR data when integrated effectively into deep learning frameworks. On the other hand, as shown in Tables 5, 6 and A2, the data  
Figure 12: Comparison of the methods on a test patch. augmentation techniques employed were found to have a significant positive impact on the performance of our change detection model, helping the model to mitigate overfitting and improve robustness; despite being SAR acquisition side-look, the image physical structures were not always preserved. Finally, the F1-score values in Tables 5 and 6 illustrate how, when dealing with a limited number of labels, the integration of a pseudo-labeling pipeline could enhance he model accuracy, without compromising its performance by introducing misclassified points associated with pseudo changes. The comparison results in Appendix A highlight the limitations of our work, reinforcing the importance of having a large and diverse training dataset, particularly for complex tasks such as open-pit mine change detection. These findings emphasize the need for further research, to concentrate on strategies for acquiring and augmenting data. By addressing the limitations imposed by the scarcity of training data, we can enhance the performance of deep learning architectures in this domain. Future works will focus on expanding the dataset to incorporate a broader range of diverse mining sites and investigating the applicability of different deep learning architectures for change detection in open-pit mines. Expanding the dataset will allow the inclusion of additional mining sites with distinct characteristics and variations in land use types. This diversity will enable our models to learn and generalize across various scenarios, improving their adaptability and accuracy in change detection tasks. In addition, expanding the dataset will also allow exploring alternative deep-learning architectures that hold potential for advancing the field of open-pit mine change detection. An example could be employing a recurrent model to manage multiple images spanning an extended time period, such as considering a sequence of three or more images.