# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Multi-Channel Attention-Based Analysis  
To address the interpretability of the trained models, in the following we focus on a multi-channel analysis of the DS-AttU architecture. All the following evaluations refer to the attention-based model trained with the BCE-MSE-F1 losses. As shown in the previous sections, the AttentionUNet backbone is characterized by the presence of some attention layers in different points of the architecture. These components are built in such a way as to allow the detection of the areas in which the network is concentrating the most attention. The patterns of model attention can drive the investigation of the correctness of the learning process of the model. Even though the DS-AttU presents four attention layers for each backbone, we concentrate our analyses on the last layer. In this way, we intend to investigate the most complete set of information the network gets before making the final prediction. Two examples of binary-class backbone output with the relative attention map are shown in Figure 5. The attention map is shown in the third column. Red pixels highlight higher values, while blue pixels highlight lower values of attention. Figure 5a shows that the attention layer output emphasizes a region that is larger than the final prediction, recalling that this mechanism has to be intended as a correlated but different process with respect to the actual network output. In the following, we inspect such a process to find relations to the network input and the resulting performance. We know from domain experts that not all the 12 channels of Sentinel-2 L2A input images share the same importance in wildfire detection. An ideal solution should focus its attention both on the right position and on the most relevant input channels. Moreover, satellite channels carry information depending on the central wavelength they are sensible at. Therefore, it could be misleading to consider each channel separately since they can be grouped using the associated spectral bands. Particularly rega  
Figure 5: Visualization of the last binary attention level compared to the network prediction. The first column is the RGB visualization of the input, the second is the binary ground truth, the third is the attention map (the intensity scale goes from blue to red for lower to higher attention values, respectively), while the last is the prediction. Rows (**a**) and (**b**) are two satellite examples from coral and cyan fold, respectively. the work in [79] shows how reflectance in vegetation zones presents peaks separating the electromagnetic spectrum, as the satellite channels, into peculiar ranges. From these considerations, we propose to analyze the channels' impact on the network by considering them grouped into domain-driven macro-bands as reported in Table 3.  
The first group (AER) consists of Band 1, which is generally used for analysis on water bodies [80] and atmosphere. Even though its central wavelength is close to bands 2, 3, and 4, we consider Band 1 in a separate group, as it is not in the range of visible wavelengths. The three RGB channels are the only ones in the human-eye visible range. They recall, with different order, the usual 3 channels of an RGB image, thus the name of the second group. The third multi-band collects all the channels in the Vegetation Red Edge (VRE) range, which is a Near-Infrared region characterized by a rapid change in reflectance of vegetation. Then, the fourth group collects the other Near-Infrared (NIR) channels and the band 9. Finally, the last group collects the remaining channels in the Short-Wave Infrared (SWIR) region. To investigate the network's ability to identify the most significant features, we performed a new test pipeline, alternatively presenting to the trained network input images obtained after an occlusion-perturbation process of some portions: such portions of the image are set to zero for each group of channels.