## 4 Results  
### Inundation Mapping Using HJ-1A CCD Data  
As described above, in the first case study, two images, acquired on 12 July 2013 (before the flood) and 27 August 2013 (after the flood), are selected for the analysis. After pre-processing, the false colour composite images of the study area and the corresponding MOD4W product used for labelling the training data are shown in Figure 5.  
The proposed spatiotemporal-context-learning-based permanent pixels-MADB (STCLP-MADB) method and three other comparison methods (K-MEANs, SP-MADB and STP-MADB) are each respectively applied to the experimental data. The final inundation mapping result for the individual method is shown in Figure 6. For a better visualization of the obtained results, we select four sub-regions and make a detailed zoom in. The location and size of the four sub-regions are shown in Figure 6e. The enlarged view of the small regions and their corresponding false colour composite, flood extraction result and the reference map are shown in Figure 7. Table 4 lists the number of inundated pixels derived by different methods in the full scene and sub-regions. From the above figures and table, some comments can be made:  
1. The proposed inundation mapping method, based on STCL permanent pixel extraction and MADB, successfully extracts most of the flood regions in the first case study. In each column of the Table 4, the STCLP-MADB method achieves the closest number of inundated pixels to the reference, except in sub-region C. It is the second best among the methods, and has almost the same number of inundated pixels as the best. All of this evidence proves the effectiveness of the HJ-1A CCD data and the proposed procedure for mapping wide inundated areas in a river flood event. 2. On the whole, it can be seen that the main regions of the flood are mostly well-delineated by each inundation mapping algorithm, except for small tributaries\(-\)for example the tributaries near the sub-region A and D\(-\)which are omitted by the SP-MADB method, and are shown in yellow. The STCLP-MADB performs better than the three other methods from the visual effect. In these regions, the K-MEANs and STP-MADB results present more false alarms, and the SP-MADB method makes more omissions. The result derived from STCLP-MADB is most consistent with \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Full Region** & **Sub-Region A** & **Sub-Region B** & **Sub-Region C** & **Sub-Region D** \\ \hline K-MEANs & 528,238 & 1833 & 16,236 & 7778 & 1442 \\ SP-MADB & 710,587 & 96 & 8470 & 5207 & 0 \\ STP-MADB & 843,570 & 1750 & 16,060 & 7009 & 1345 \\ STCLP-MADB & 812,610 & 1308 & 13,884 & 7011 & 1047 \\ Reference Map & 764,470 & 1176 & 11,861 & 6267 & 873 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The first case study in 2013—Number of inundated pixels. Figure 5: False colour composites (R4G3B2) of HJ-1A CCD images acquired on (**a**) 12 July 2013 (before the flood) and (**b**) 27 August 2013 (after the flood) for the first case study. (**c**) Corresponding MOD4W water mask product with water in blue and land in black. the reference map. Its effectiveness for precision mapping is significant for inferring the future evolution of the flood. 3. From the detailed mapping results, it can be found that the inundated regions are delineated differently by different methods. In the map derived using K-MEANs, many points of false positive can be found in the unflooded regions. However, the SP-MADB method produces more false negatives in some small flood regions and half-submerged regions. More advanced results are obtained by STP-MADB and STCLP-MADB methods. Further comparisons of the details show that the results from STCLP-MADB provide finer outlines and are slightly better. 4. Although the results from STCLP-MADB are quite promising, there are still some false positive errors, mainly occurring in the small unflooded areas surrounded by large flooded areas.