## 4 Results  
We evaluate all thirty-six resulting combinations of the architectures, loss functions, and image types introduced in Section 3. For all image types, we split the Corsican FireDatabase into a training set that, after data augmentation, contains 8192 images and a test set comprised of 128. We use fixed hyper-parameters for all training runs, with a batch size of four (due to hardware constraints), a learning rate of \(10^{-4}\), ADAM optimizer, and 100 training epochs. We conducted the training on an NVIDIA DGX workstation using two NVIDIA P100 GPUs and the TensorFlow framework. First, we identify in Table 2 the best five performing combinations per metric. We can observe that, for all metrics, the _Akhloufi + Dice + Visible_ combination shows the best results, albeit by a close margin. Additionally, we can observe a high presence of the _Akhloufi_ architecture and the _Focal Tversky_ loss in the top five for all metrics. It is also relevant to note that the visible images consistently appear in the top three combinations, with the NIR images also present, and the _FIRe-GAN_ images, that is, fused images produced by the _FIRe-GAN_ method, are the only fused ones to appear in the best performing combinations. In Figure 15, we show sample segmentation results for the best three combinations of Table 2 for all metrics. Additionally, we are interested in the parameters (architecture, loss function, and image type) that allow for robust and consistent results. We then group the results by these parameters and visualize their performance across all metrics to observe the variability in the results. In Figure 16, we show the scores for all metrics grouped by architecture (Figure 16a-c), image type (Figure 16d-f) and loss function (Figure 16g-i). Figure 15: Sample segmentation results for the top three combinations for all metrics. For the segmentation results of the _Choi_ architecture, we binarized the images by assigning a value of 1 to all non-zero pixels in a post-processing step. We can observe that the _Akhloufi_ architecture and the _Focal Tversky_ loss show by far the most robust results, displaying high and consistent scores across all metrics. In contrast, the image type appears to have very little influence on the segmentation performance, as the results are considerably similar for all image types, with only a slight advantage for the visible images on the MCC and F1 metrics. In Figure 17, we can see the resulting segmentation masks for the _Akhloufi_ + _Focal Tversky_ combination for all image types to visually assess the similarity in the results. Figure 16: Results grouped by architecture (first row), image type (second row), and loss function (third row) for all metrics. Next, we obtain and visualize in Figure 18 the Spearman correlation matrix of every parameter to evaluate its monotonic correlation with the evaluation metrics. We can observe that the architecture and loss function parameters show strong correlations with the evaluation metrics, with the _Akhloufi_ architecture and _Focal Tversky_ loss displaying a strong positive correlation with the evaluation metrics. Regarding the image type parameter, we can observe significantly weak correlations with the evaluation metrics for all image types, with the visible images displaying a weak positive correlation with the evaluation metrics. Additionally, we can observe a near-perfect positive correlation between the three evaluation metrics. The latter indicates that for future works, one of these metrics is sufficient to evaluate the performance of a model when working with the Corsican Fire Database.