# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Houston Flood Case  
Figure 2a shows the intensity RGB composite (R = pre-event, G = B = co-event) of the Houston study area. The red color indicates non-obstructed-flood, such as flooded bare soils or wholly submersed short vegetations, without double-bounce occurring between the water surface and buildings/tree trunks. The cyan color depicts the flooded buildings or partially submersed vegetation where the enhanced double-bounce between the water surface and buildings/tree trunks incurs an increase in the co-event \(\sigma^{\circ}\). The coherence RGB composite (R = pre-event, G = B = co-event) is shown in Figure 2b. The white color shows non-flooded built-up areas which are characterized by high \(\gamma\) in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition Time** & \(B_{t}\) **(days)** & \(B_{p}\) **(m)** &
\begin{tabular}{c} **Window Size** \\ **(Range \(\times\) Azimuth)** \\ \end{tabular} \\ \hline
29/08 – 02/01 & 126 & 150 & \(7\times 7\) \\
02/01 – 13/02 & 42 & 47 & \(7\times 7\) \\
13/02 – 31/07 & 168 & 221 & \(7\times 7\) \\
31/07 – 11/09 & 42 & 123 & \(7\times 7\) \\
**11/09 – 23/10** & 42 & 35 & \(7\times 7\) \\ \hline
23/10 – 29/01 & 98 & 205 & \(7\times 7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ALOS-2/PASAR-2 coherence data used for the Joso case study (flood acquisition is marked in blue color). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition Time** & **Polarization** & **Incidence Angle (\({}^{\circ}\))** & **Resolution (m)** & **Orbit** \\ \hline
29/08/14 & HH & 35.4 & 3 & Ascending \\
02/01/15 & HH & 35.4 & 3 & Ascending \\
13/02/15 & HH & 35.4 & 3 & Ascending \\
31/07/15 & HH & 35.4 & 3 & Ascending \\
**11/09/15** & HH & **35.4** & **3** & Ascending \\ \hline
23/10/15 & HH & 35.4 & 3 & Ascending \\
29/01/16 & HH & 35.4 & 3 & Ascending \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ALOS-2/PASAR-2 intensity data used for the Joso case study (flood acquisition is marked in blue color). both pre-event and co-event acquisitions. The appearance of floodwater between buildings results in a significant drop-off in co-event \(\gamma\) which are illustrated in the red color. However, the drop-off in \(\gamma\) could also be owing to random variation of vegetation (note the widely spread red color), thus the temporal non-coherent targets should be masked out when using \(\gamma\) in flood detection, as we discussed in Section 2. In Figure 2c, the RGB composite of intensity and coherence is adopted (R = co-event \(\sigma^{\circ}\), G = pre-event \(\gamma\), and B = co-event \(\gamma\)). The flooded built-up areas are discernible in yellow color (e.g., high co-event \(\sigma^{\circ}\), high pre-event \(\gamma\), and low co-event \(\gamma\)). The green color could be related to flooded bare soils with sparse meadow which are characterized by low co-event \(\sigma^{\circ}\), medium pre-event \(\gamma\), and low co-event \(\gamma\). Non-flooded built-up areas are shown in white color. Besides, the study area is quite vegetated, some small houses are encircled by trees, the mixed backscattering of the aforementioned objects could be presented in a single pixel of the medium resolution (e.g., 20 m) Sentinel-1 data, thus attenuating the double-bounce effect of buildings and reducing the values of both \(\sigma^{\circ}\) and \(\gamma\). These areas can be found in brown color in Figure 2c. The quantitative evaluations of the flood extent in the study area (Figure 2) produced by the fusion of \(\sigma^{\circ}\) and \(\gamma\) (Figure 3a) and \(\sigma^{\circ}\) alone (Figure 3c) are reported in Table 5. Although high values of OA (e.g., 94.5% vs. 93.7%) are achieved for both scenarios, OA is an inappropriate evaluation metric for this case due to the unbalanced extent of the classes (e.g., flood class occupies around 10% of the whole area). When \(\sigma^{\circ}\) and \(\gamma\) are synergistically used, \(\kappa\) is around 0.68 and F1 score is around 0.70.