## 3 Methods  
### Permanent Pixel Extraction Using Spatiotemporal Context Learning  
The images before and after a flood are referred to as image #1 and image #2, respectively. The pixels with a constant land cover type, no matter what the type is, are defined as permanent pixels. The proposed method is divided into two steps. First, the permanent pixels in image #1 and image #2 are extracted based on the STCL strategy. This is a method that models the relative relationship between an object and its context. We introduce it to formulate the relationship between a satellite image pixel and its context. Through comparing the models at different time points, a confidence value for whether \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite Sensor** & **Band No.** & **Spectral Range (um)** & **Spatial Resolution (m)** & **Width** \\ \hline  & 1 & 0.45–0.52 & & \\ GF-1 WFV & 2 & 0.52–0.59 & & \\  & 3 & 0.63–0.69 & 16 & 800 km \\  & 4 & 0.77–0.89 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Technical parameters of the GF-1 WFV data. a pixel changes or not is calculated to extract the permanent pixels. Second, using these permanent pixels as a training set, a widely adopted machine learning classifier, Modest AdaBoost, is trained and implemented for mapping inundation in image #2. Modest AdaBoost is one of the derivations of the boosting algorithm, like the original AdaBoost algorithm. It combines the performance of a set of weak classifiers, and also proves better than other boosting algorithms for convergence ability. More details about these methods will be given below. In this section, we will first discuss the procedure in the first step. Due to its capacity for targeting specific land cover type and reducing influence from inconstant band representation, spectral indices are commonly used in diverse remote sensing applications, such as disaster monitoring, land cover mapping and disease prevention [26; 27; 28]. For mapping different cover types in different applications, various indices have been proposed, including the normalised difference vegetation index (NDVI), the enhanced vegetation index (EVI), NDWI, and the normalised difference built-up index (NDBI) and so on. Among these indices, the NDWI has been successfully applied to mapping land surface water, and proved more effective than other general feature classification methods [29]. In this study, we calculate the NDWI in the experimental datasets (the HJ-1A CCD data for the first case study and the GF-4 PMS data for the second case study) first. Then, the steps for extracting permanent pixels will be executed on the NDWI data. The NDWI is calculated as:  
\[\mathrm{NDWI}=\frac{\mathrm{Green-NIR}}{\mathrm{Green+NIR}} \tag{1}\]  
where Green and NIR are the reflected green and near infrared radiance, respectively, which are replaced by band 2 and band 4 in the HJ1-A CCD data case, and band 3 and band 5 in the GF-4 PMS data case [15]. NDWI can eliminate the influence from the band value difference, but not the influence caused by the different weather conditions. However, as it is the relative relationship between neighbouring pixels that we use, influences from changes of overall brightness are limited in the proposed procedure. In the visual tracking field, as the video frames usually change continuously, a strong spatiotemporal correlation is thought to exist between a target and its surroundings. In order to make better use of this relative relationship, [PERSON] et al. [30] proposed the STCL method. In this method, a rectangular contextual region was first built with the target in the centre. With the low-level features (including the image density and location) of the contextual region, the relative relationship between the target and its surroundings in contextual region was modelled. When a new frame came, it was put into the model to calculate a confidence map, indicating the location that best matched the contextual relationship of previous frames. This location was the inferred location of the target in the new frame.