## 3 Results
In order to compare the dNBR with the GT, its values were thresholded according to the default values [23]. In this case, best results vary from fold to fold, but generally, they are matched by U-Net based approaches. It must be considered that the dNBR is computed using both pre- and post-fire acquisitions, whereas U-Net's approaches consider only post-fire acquisitions. In order to summarize the performance, the average RMSE value for each severity level is shown in Table 6. Compared to the Single U-Net, the Double-Step U-Net results to be a better approach, achieving the best RMSE on each severity level. Moreover, with reference to the dNBR, the Double-Step U-Net achieves comparable performance, with a noticeable improvement for the detection of the unburned area (severity 0), but using only half of the information. The reason behind the success of Double-Step U-Net is hidden in the problem split. First, the neurons of the Binary U-Net are employed to identify burned regions. Its prediction will mask the spectral values of unburned regions, leaving \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fold**} & \multirow{2}{*}{**Severity**} & \multicolumn{5}{c}{**Performance (RMSE)**} \\ \cline{3-6}  & & **dNBR** & **Single U-Net** & **Parallel U-Net** & **Double-Step U-Net** \\ \hline \multirow{4}{*}{Blue} & 0 & 0.78 & 1.06 & 0.23 * & 0.27 \\  & 1 & 1.07 & 0.89 & 0.89 & 0.73 * \\  & 2 & 1.23 & 0.71 & 0.80 & 0.62 * \\  & 3 & 0.82 & 0.63 & 0.65 & 0.52 * \\  & 4 & 0.62 \({}^{\dagger}\) & 0.93 * & 0.96 & 1.44 \\ \hline \multirow{4}{*}{Brown} & 0 & 0.65 & 0.22 & 0.20 * & 0.47 \\  & 1 & 0.97 & 0.94 & 0.94 & 0.92 * \\  & 2 & 1.01 & 0.65 * & 0.65 * & 0.86 \\  & 3 & 0.70 & 0.35 * & 0.35 * & 0.39 \\  & 4 & 0.48 \({}^{\dagger}\) & 1.26 * & 1.28 & 1.49 \\ \hline \multirow{4}{*}{Fucsia} & 0 & 0.82 & 0.39 & 0.16 * & 0.24 \\  & 1 & 1.37 & 1.40 & 1.41 & 1.02 * \\  & 2 & 1.12 & 1.35 & 1.35 & 1.00 * \\  & 3 & 1.10 & 0.97 & 0.97 & 0.75 * \\  & 4 & 1.67 & 1.26 * & 1.28 & 1.49 \\ \hline \multirow{4}{*}{Green} & 0 & 0.20 & 0.28 & 0.04 * & 0.18 \\  & 1 & 0.64 \({}^{\dagger}\) & 1.03 & 0.92 & 0.80 * \\  & 2 & 1.18 \({}^{\dagger}\) & 1.78 & 1.76 & 1.40 * \\  & 3 & 1.46 & 1.87 & 1.90 & 1.38 * \\  & 4 & 1.09 & 1.57 & 1.58 & 1.00 * \\ \hline \multirow{4}{*}{Orange} & 0 & 0.42 \({}^{\dagger}\) & 0.40 & 0.39 * & 0.43 \\  & 1 & 1.10 \({}^{\dagger}\) & 1.68 & 1.68 & 1.47 * \\  & 2 & 1.04 & 1.14 & 1.14 & 1.02 * \\  & 3 & - & - & - & - \\  & 4 & - & - & - & - \\ \hline \multirow{4}{*}{Red} & 0 & 0.20 & 0.21 & 0.15 * & 0.33 \\  & 1 & 0.66 \({}^{\dagger}\) & 0.71 * & 0.71 * & 1.21 \\  & 2 & 0.80 & 0.56 * & 0.56 * & 0.97 \\  & 3 & - & - & - & - \\  & 4 & 0.58 \({}^{\dagger}\) & 1.96 & 1.96 & 1.21 * \\ \hline \multirow{4}{*}{Yellow} & 0 & 1.31 & 0.37 & 0.25 * & 0.54 \\  & 1 & 0.83 \({}^{\dagger}\) & 0.83* & 0.84 & 1.04 \\ \cline{1-1}  & 2 & 1.24 & 0.89 & 0.89 & 0.71 * \\ \cline{1-1}  & 3 & - & - & - & - \\ \cline{1-1}  & 4 & 0.99 \({}^{\dagger}\) & 1.70 & 1.71 & 1.18 * \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-validation performance per fold. (\({}^{\star}\)) indicates the best RMSE per severity category among the three U-Net versions. (\({}^{\dagger}\)) indicates the best RMSE per severity category, dNBR included. only the information related to burned areas to the Regression U-Net. Therefore, the latter network will employ its neurons in finding differences between correlated values (severity levels 1 to 4). It is worth mentioning that the masking operation performed by the Binary U-Net prediction introduces a new and uncommon value in the spectral information fed as input to the Regression U-Net: the 0 value. Areas identified as unburned will be \"cancelled\" by replacing their original value with 0, that is not present in nature. Therefore, a bad classification from the Binary U-Net can lead the Regression U-Net to make more mistakes because it will consider 0-valued-regions as unburned and every other unburned region not detected by the Binary U-Net will be considered as burned.