# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Multi-Channel Attention-Based Analysis
#### 4.6.1 Binary Backbone  
The plots shown in Figure 7 collect the results of the occlusion strategy described in the previous section, applied to the last level of the binary-class backbone. Every row of the graph represents the comparison between the performance distributions in terms of Recall (RE), Precision (PR), F\({}_{1}\) Score, and Intersection over Union (IoU). The analysis is repeated for each channel group as the column changes. Table 4 collects instead the \(\text{D}_{\text{KL}}\) of the F\({}_{1}\) score distribution for the three occlusion alternatives and a normalized version of the \(\Delta\text{D}_{\text{KL}}\), normalized with respect to the \(\text{D}_{\text{KL}}^{\text{all}}\) for the different channel groups. This last parameter is presented in order to investigate the effect of the attention-guided perturbation in terms of the maximum divergence of the group. The resulting measure is then calculated as the ratio \[\delta\text{D}_{\text{KL}}=\frac{\Delta\text{D}_{\text{KL}}}{\text{D}_{\text{KL}}^ {\text{all}}}\,. \tag{3}\]  
From these results, we can state that the less relevant Multi-band is AER, as it gets the lowest value of \(\text{D}_{\text{KL}}^{\text{all}}\) in terms of F1 score. Looking at the Recall distribution, we observe that the all_occluded distribution is particularly close to the normal one. This indicates that most of the detected burnt pixels are preserved after the perturbation. The worsening of the Precision curve shows instead that the unburnt pixel detection ability is not preserved. Then we could say that the network without the AER band tends to identify more wildfires than with the full information, resulting in an overall decreasing in performance as shown in F1 score and IoU distributions. Moving to the RGB Multi-band, it appears as the most significant one, as the \(\text{D}_{\text{KL}}^{\text{all}}\) presents the maximum value among the groups. The associated Recall curve, which is \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Multi-Band** & \(\delta\text{D}_{\text{KL}}\) & \(\text{D}_{\text{KL}}^{-}\) & \(\text{D}_{\text{KL}}^{+}\) & \(\text{D}_{\text{KL}}^{\text{all}}\) \\ \hline AER & 0.75 & 0.02 & 0.64 & 0.83 \\ RGB & 0.60 & 0.04 & 1.36 & 2.19 \\ VRE & 0.73 & 0.03 & 0.97 & 1.30 \\ NIR & 0.81 & 0.04 & 0.74 & 0.87 \\ SWIR & 0.78 & 0.03 & 0.95 & 1.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Divergences for the F1 score distributions. The first column shows the difference between the up- and down-attention occlusion as in Equation (3). The other ones are the values are the \(\text{D}_{\text{KL}}(P||Q^{*})\) where \(*\) stays for \(-,+\) and all. Figure 7: Comparison of the binary prediction for each channel group between the normal case and the different occlusion strategies. The _normal_ (blue line) distribution is the same for each row, while the y-scale is different across columns in order to fit the other curves. highly peaked at 0, suggests that most of the wildfires are not detected anymore with this kind of perturbation. On the other side, as shown in the Precision curve, the degradation is not mitigated by the detection of 0-labeled pixels, then a general prediction in this way would probably present a high number of unburnt pixels and most of the burnt ones pointing to wrong areas. In contrast with the significance of the Multi-band, we can also observe that the \(\delta\)D\({}_{\text{KL}}\) is the lowest among the resulting values. Therefore, the network selects these channels as relevant information, as shown by the absolute values of the single divergences, but a higher focus is probably given to other groups. VRE and NIR present similar trends with respect to each other and, still having slightly better performance, to the previous one. Therefore, we again expect a general prediction with a higher presence of correctly identified unburnt pixels than burnt ones. Anyway, this degradation is less pronounced when perturbing the NIR group in both the Precision and the Recall performance.