# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## Appendix B
### Data Augmentation Techniques  
Data augmentation means artificially increasing the dataset's size by applying various transformations to the original data. This can help to prevent overfitting, improve generalization, and increase the robustness of the model to variations in the input data. In this work, in addition to the classic geometric transformations, i.e., vertical/horizontal flip and shear, we have also introduced channel swap and coherence scaling as custom data augmentation for the change detection task. Note that, for all these techniques, we set a probability of 50% for an image to be used in the original or augmented version.  
Geometric transformations are simple and inexpensive to implement in computer vision tasks. The most common one is flipping, which involves creating a mirror image of the original data. When flipping an image along its horizontal or vertical axis, the resulting image appears to be rotated by 180 degrees. It is essential to highlight that since the SAR image is acquired in side look mode, the horizontal flip does not alter the acquisition geometry. On the other hand, the vertical flip makes the geometric distortions appear on the opposite side. The last geometric data augmentation employed is the shear, which is a transformation that changes the shape of image distorting them along a selected axis. Also in this case, being SAR acquisition side-look, the SAR image physical structures are not preserved. In our experminet, we randomly set the shear rotation to be in the y-axis angle range \([-10^{\circ},+10^{\circ}]\).  
As previously discussed, input channel swapping is one of the custom data augmentation techniques utilized in the change detection task. This technique involves swapping the order of the input images in the model, \(x_{before}\) and \(x_{after}\). The primary objective of this operation is to ensure that the model is invariant to the temporal order of the images and can accurately detect changes regardless of whether they occurred in the past or present. By swapping the input channels, the model is exposed to the same spatial features and image structure but with the temporal order reversed. This allows the model to learn to distinguish changes between two images based solely on the spatial content and not the order of the images. In addition to the previously discussed custom data augmentation techniques, another method was implemented specifically for the coherence information in the change detection task. This approach involves randomly scaling the coherence values in areas without change, as described in Equation (9). The primary objective of this technique is to prevent the network from relying solely on simple thresholding of coherence, which can lead to inaccuracies and reduced performance in areas with low coherence values, such as forests or grass. By randomly scaling the coherence values, the model is exposed to a wider range of coherence levels in non-change areas, which can help it to be more robust and adaptive to varying levels of coherence. This custom data augmentation technique is particularly useful in remote sensing applications where coherence may be used as a key indicator of changes in the landscape.