## 4 Results
### Inundation Mapping Using GF-4 PMS Data  
The second case study aims to analyse the GF-4 PMS data for the 2016 flood event at Dongting Lake. Figure 9 shows the two images selected for this case, which were acquired on 17 June 2016 (before the flood) and 23 July 2016 (after the flood). The corresponding MOD44W product used as the ancillary data is also shown in Figure 9.  
In the second case study, inundation mapping results using different strategies are shown in Figure 10. Similar to the first case study, four sub-regions located at different positions are selected and shown in Figure 11, which aims to visually compare the results in a more detailed way. With regards to quantitative evaluation, Tables 6 and 7 report the number of inundated pixels and the final accuracy values, respectively. Figure 12 illustrates the relation between the permanent pixel proportion and detection accuracy. As can be seen from the figures and the table, many similarities exist between the results of the second and the first case studies, and several slight differences as well. They are described as follows:  
1. In terms of the performance in categorisation, results in the second test are similar to that in the first test. The proposed STCLP-MADB method still achieves the best overall accuracy and kappa coefficient. K-MEANs and STP-MADB methods achieve the best omission and commission, respectively, while STCLP-MADB shows an average performance of these two rates. As the two test datasets are from different sensors, locations and inundation cases, this experiment further proves the good robustness of the proposed method. 2. K-MEANs makes use of the statistical properties of the whole image, which causes high commission because the inundated pixels can have a different appearance in different contextual situations. SP-MADB and STP-MADB draw more attention to the local characteristics, but they make the determination of permanent pixels by counting, which lacks a theoretical foundation and can be easily disturbed. This can be found by comparing Figures 6c and 10c. In the first case, using HJ-1A CCD data, the STP-MADB method produces more commission, while in the second case study, using GF-4 PMS data, more omission than commission is introduced in the STP-MADB result. In the proposed method, a spatiotemporal context confidence calculating model is adopted to overcome the limitation of counting. With the formulised combination of local spatiotemporal and spectral information, we achieve a more accurate and robust inundation map than other methods. 3. The changing curves of accuracy with \(n\) are more unstable than those in the first test. The influence of \(n\) on result precision does not change monotonically. It is difficult to find any rules in the curves at all. This could be because the outline of the inundation is more complicated in the second case study than in the first. Moreover, the spatial resolution of the GF-4 PMS data is sparser than that of the HJ-1A CCD data, which brings out more mixed pixels. With the increase in these uncertainties, the variation in accuracy becomes more unpredictable. Nevertheless, the fluctuation is still within a limited range. The effectiveness of the proposed method is rather stable. 4. As the GF-4 satellite was officially put into service not long ago (in June 2016), research on GF-4 PMS data is rare. Our work explores the applied value of this new dataset and proves its effectiveness for inundation mapping. More promising research about GF-4 PMS data could be carried out in the future. \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 & 2,417,535 & 74,610 & 21,785 & 10,363 & 13,001 \\ SP-MADB & 1,103,408 & 23,946 & 1488 & 5884 & 6163 \\ STP-MADB & 873,738 & 16,908 & 1468 & 5233 & 3848 \\ STCLP-MADB & 1,215,464 & 30,502 & 2116 & 6668 & 6961 \\ Reference Map & 1,357,670 & 37,529 & 3654 & 7963 & 7510 \\ \hline \hline \end{tabular}
\end{table}