## 5 Discussion
Extensive literature shows that the precision of supervised classification methods is generally better than that of unsupervised classification methods. Unsupervised flood mapping methods, like K-MEANs, can bring about more errors in scenes of large area or complicated distribution. Because in these situations, the radiation value of water may vary a lot at different locations. Without a learning strategy, some non-water pixels with similar features to the water at other locations could be identified as water, as can be seen in Figure 10a of the Dongting Lake case. With the aid of the samples, the supervised classifier can learn and adapt itself better to different land cover characteristics in different scenes, resulting in higher accuracy. But the samples usually need to be selected manually, which limits their applicability in disaster response. Another essential advantage of the proposed method is that it proposes an automatic sample selection method, and combines it with a learning method. With the advantages of these two methods, both good precision and automation can be achieved. The utilisation of local information is also a factor bringing improvement to the result. On one hand, it is more robust to utilise both contextual and global information than to utilise global information only. On the other hand, the experiment results show that the proposed method outperforms (qualitatively and quantitatively) the SP-MADB, STP-MADB methods. The only distinction among these three methods is the permanent pixel extraction strategy. All three methods utilise the local relationship between a pixel and its surroundings. The SP-MADB and STP-MADB methods count the number of 8-neighbourhood or 17-neighbourhood pixels with equal mean or medium to the object pixel. Noise and radiation variation, which exist all the time, can easily change the count result. Moreover, if a pixel and its neighbouring pixels simultaneously change from one cover type to another, the mean and medium will still remain the same, leading to errors in the permanent pixel set. From the experiment results we can see that the STP-MADB method obviously makes more commissions than omissions in the first case study, but makes more omissions in the second case, which proves its lack of robustness. Whereas, the proposed method builds a model between the pixel and its neighbouring regions, instead of counting the few adjacent pixels. Even if there are some noise pixels, the general structure of the model will not change. With better selection of the permanent pixels and the training set, the STCLP-MADB method produces a more precise outline of the inundated areas. This study is proposed for floods, which is a practical problem. Hence it makes sense that this proposed method can be applied operationally, and that it can help when a real flood comes. Here we propose some suggestions for implementation, which may help the STCL-MADB method to be effectively applied in a real application. The whole workflow can be divided into three steps: extracting permanent pixels using STCL, training the classifier and mapping the inundation. The first step, especially the STCL algorithm, accounts for most of the time consumption in the whole process. Not only because the STCL algorithm has higher computation complexity than other steps, but the operations need to be performed pixel by pixel. For example, in the second case study at Dongting Lake, the size of the data is 2534 \(\times\) 2235 pixels. The first step takes around 3 days, while the second and third steps take around 15 min and a few dozen seconds, respectively. All these experiments are implemented by MATLAB 2013 on a laptop with an i7-4710 HQ CPU and 8 GB RAM. In practice, a library of permanent pixels can be built in advance for regions with high flood risk. With the accumulation of time series data, the library can be updated continuously. Then, the classifier can also be trained and updated with the new library.