## 4 Results and Analysis  
### Urban Burning Area Detection  
To verify the effectiveness of our method, we randomly selected a number of combustion areas and confusing objects (beach and highlighted building tops) on two types of optical images and compared the response values before and after the method improvement. As shown in Figure 8, IFB-CEM can significantly suppress the response values of non-burning areas in Landsat-8/9 and Sentinel-2A/B images and amplify the response values of some pixels in burning areas, which has a more obvious effect in Sentinel-2 images. (These pixels are usually burning centers, and their response is low in the original CEM algorithm due to the saturation of their response at bands 11 and 12 of Sentinel-2A/B's sensor). In the local figure at the bottom right corner of Figure 8, IFB-CEM (split by horizontal lines) eliminates background interference to a greater extent and preserves target pixels more completely. Figure 7: Coherence maps of different polarization before the conflict. We calculated recall, precision and F1 scores under different thresholds by adjusting response thresholds. Recall is defined as the proportion of correctly extracted burning areas to the number of all actual burning areas, and precision is defined as the proportion of correctly extracted burning areas to the number of all extracted burning areas. The F1 score is defined as the harmonic mean of recall and precision. Since the boundary of the combustion area cannot be accurately divided, we believe that the extracted combustion area and Ground Truth overlap degree is more significant than 0.8, which means that the combustion area at this location is successfully extracted. The best response threshold was determined by the highest F1 score. Table 1 shows the best detection effect of CEM and IFB-CEM methods on each scene. Since the IFB-CEM method suppressed the response of non-target areas, the error detection rate was significantly reduced, and the precision was generally significantly improved. \[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1\ Score=\frac{2\cdot Recall\cdot Precision}{Recall+Precision} \tag{10}\]  
where \(TP\), \(TN\) and \(FP\) are the true positive, true negative and false positive, respectively. Figure 9 shows the temporal and spatial distribution of burning areas during the conflict. Before and at the beginning of the conflict (before 14 March 2022), the thermal anomalies were distributed in the Ilyich Iron and Steel Works in the north of the city and the Azovstal Steel and iron works in the south of the city, indicating that the production and living activities of the residents were proceeding normally. Images from 14 March showed that the thermal anomaly in the factory area disappeared, meaning that industrial production had to stop due to suffering attack and that all the thermal anomalies since then have been ground objects burning due to the conflict. The Russian forces mainly attacked from east, west and north three directions. The burning area of the city increased dramatically in a short time, showing a trend of gradual encirclement and contraction towards the city center. By the end of March, Russian forces had largely completed the siege of the Azovstal steel and iron works, after consolidating their positions in the city center, had launched an offensive against the Ukrainian 36 th Marine Brigade stationed at the Ilyich Iron and Steel Works in the north and had captured the city hall of the Kalmiusky district. In early April, Russian forces began advancing southwest from the city center toward the coast in an attempt to encircle and eliminate Ukrainian forces in the port area, and the burning area shifted accordingly. By the end of April, the thermal anomaly area in the urban area was reduced to the lowest level, and the remnants of the Ukrainian army all  
Figure 8: The response values of ground objects before and after the method improvement. retreated to the Azovstal steel and iron works.