## 4 Results
### Inundation Mapping Using HJ-1A CCD Data
For example, in Figure 6d, we can find some pixels in blue inside the main region of the flood, which are unflooded areas but determined as flood by the STCLP-MADB method. This is because these areas mostly comprise mixed pixels. Different proportions and locations of water in one mixed pixel influence what class the pixel is distributed to. Figure 6: (**a–d**) Flood inundation mapping results for the first case study using K-MEANs, SP-MADB, STP-MADB and STCLP-MADB methods. (Gray: flood pixels in both the detection and reference maps; Blue: flood pixels only in the detection map; Yellow: flood pixels only in the reference map; Black: the background). (**e**) The locations of the four sub-regions in red rectangle, shown on the reference map of inundation derived from the GF-1 WFV data, with the water in yellow and the background in black. be calculated, among which the overall accuracy is the rate of correctly classified water and non-water pixels among the total pixels. The values are shown in Table 5.  
From the numbers reported in Table 5, a few further conclusions can be summarised:  
1. STCLP-MADB achieves the highest overall accuracy and kappa coefficient among these four methods, which shows that STCLP-MADB performs better than the others in terms of quantitative evaluation. Extending the SP strategy to STP strategy improves mapping accuracy. Furthermore, utilising STCL confidence calculation instead of a simple counting strategy in STP also enhances the mapping results. 2. With incomplete flood information, different flood detectors produce different commission and omission errors. The best omission and commission rates are achieved by the K-MEANs and SP-MADB methods, respectively. However, there is always a balance between the omission and the commission. A decrease in omission errors usually brings about an increase in commission errors and vice versa. As can be seen from Table 5, the high commission and omission rates limit the ability of K-MEANs and SP-MADB methods in inundation mapping, which is illustrated in Figures 6 and 7, while the STCLP-MADB method achieves a balance between these two rates and provides a more acceptable result. We also discuss the relation between accuracy of the proposed method and the \(n\) in the permanent pixels extraction step, i.e., the influence that the number of selected permanent pixels has over mapping precision. The result is shown in Figure 8 below. In this work, we try \(n=1,2,\ldots,9\), for if \(n\) is too big, it will cost a lot of computation resources and time for training the classifier, which is impractical and cannot satisfy the need for a quick response to a disaster. From the figure, it can be seen that more permanent pixels leads to an increase in commission and decrease in omission, but this only happens when \(n\leq 2\). When the value of \(n\) gets higher, there is no significant change in commission and omission. Similarly, for overall accuracy, there is only a slight decrease (around 0.1%) when \(n\) changes from 1 to 2. After that, the overall accuracy remains almost unchanged. Therefore, it can be concluded that for the proposed STCLP-MADB method using HJ-1A CCD data, the number of permanent pixels has a very limited influence on mapping precision. Given the importance of computation efficiency in disaster response, it is quite enough to set \(n\) as 1 or 2. Figure 8: The first case study: commission, omission and overall accuracy in function of \(n\), the percentage of permanent pixels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Overall Accuracy (\%)** & **Kappa** & **Omission (\%)** & **Commission (\%)** \\ \hline K-MEANs & 87.48 & 0.7450 & 2.77 & 17.51 \\ SP-MADB & 90.73 & 0.8146 & 12.18 & 5.53 \\ STP-MADB & 91.22 & 0.8220 & 3.03 & 12.13 \\ STCLP-MADB & 92.25 & 0.8435 & 4.10 & 9.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The first case study in 2013—Accuracy.