# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Multi-Channel Attention-Based Analysis
We chose zero as perturbation value because it was already used in case of unavailable or invalid data: in the case of missing data from satellites, invalid pixels were encoded with zeros. Furthermore, such encoding is also used by the binary-class backbone, which masks the identified non-burned areas with zero-valued pixels. Hence, the usage of zero-valued pixels for the occlusion-perturbation mechanism in the analysis of the attention-guided explainability is motivated by the pre-existing encoding of invalid and to-be-ignored pixels. Specifically, we applied three different types of perturbations:  
* The all_occlusion perturbation sets to zero all the pixels belonging to the given macro-band group (i.e., all its channels/bands). * The up_attention_occlusion perturbation sets to zero all the pixels belonging to the given macro-band and with an attention value _greater_ than a defined _threshold_. * The down_attention_occlusion perturbation, instead, sets to zero all the pixels belonging to the given macro-band and with an attention value _lower_ than a defined _threshold_. For instance, for the _RGB Group_, the all_occlusion perturbation sets to 0 all the pixels of the R, G, B channels; the up_attention_occlusion perturbation only the pixels of the R, G, B channels with attention greater than the threshold; and down_attention_occlusion perturbation only the pixels of the R, G, B channels with attention lower than the threshold \begin{table}
\begin{tabular}{l l c} \hline \hline
**Group** & **Sentinel-2 L2A Band** & **Central Wavelength (nm)** \\ \hline AER & Band 1–Coastal aerosol & 442.7 \\ \hline  & Band 2–Blue & 492.4 \\ RGB & Band 3–Green & 559.8 \\  & Band 4–Red & 664.6 \\ \hline  & Band 5–Vegetation red edge & 704.1 \\ VRE & Band 6–Vegetation red edge & 740.5 \\  & Band 7–Vegetation red edge & 782.8 \\ \hline  & Band 8–NIR & 832.8 \\ NIR & Band 8A–Narrow NIR & 864.7 \\  & Band 9–Water vapour & 945.1 \\ \hline SWIR & Band 11–SWIR & 1613.7 \\  & Band 12–SWIR & 2202.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Band aggregations following the common responses to terrain bodies. (for all inputs in the dataset). The process is repeated and produces a new perturbed version of the dataset for each group and for each perturbation type. The model's performance on the entire original dataset is compared with each perturbed version of the dataset and may be affected by the perturbation in one of the following cases:  
* If the performance _decreases_, the perturbation negatively affected the model, and therefore the original perturbed inputs were _positively impacting_ on the original correct model's predictions. * If the performance _remains the same_, the perturbed inputs were _neutral_ for the model. * Finally, if the performance _increases_, the perturbed inputs were _negatively impacting_ the model's behavior on the original inputs. We expect that the most relevant macro-bands groups would significantly affect performance as they are supposed to be fundamental in the prediction. In contrast, the least significant macro-bands groups would not considerably affect performance. Finally, if the perturbation of some macro-bands groups will increase the performance, then the model learned some pattern related to those groups incorrectly. Moreover, we expect that a totally unreliable attention layer would present comparable performance when excluding high-attention and low-attention zones. Instead, an effective attention layer would be associated with a moderate loss in performance between the original images and the ones obtained by removing low-attention points and a high loss in performance between the original images and the ones with the perturbation of high-attention points. In the following, we apply this strategy to the DS-AttU with the best loss configuration. While this procedure can be used for all four attention levels of the network, we decided to analyze the last one for each of the backbones, expecting that such positions would reflect the highest single-network degree of learning.