## 3 Methodology
### Extraction of Surface Water by SMDPSO+LFE Method  
For a particular image resolution, a river may appear to be about the width of a single-pixel or more, depending on the actual width of the river [39]. Figure 7 shows the feature differences between rivers with a single-pixel width and rivers with multiple pixels in the water probability image. In general, the techniques used to extract the feature width of a single pixel cannot be used directly to extract the same features at a higher resolution. A single-pixel width element is mapped by a single line in the generated vector database, while the multiple linear elements of pixel width must be represented as a set of polygons [40]. Figure 5: Rough classification flow chart of SMDPSO. Figure 6: Rough classification result graph of SMDPSO. SMDPSO+LFE method calculates the probability of the water body through the standard spectrum, and roughly classifies the image through the probability of the water body. The results of the rough classification are divided into two parts. As the flow chart is shown in Figure 8, the first part is used to extract water pixels via the DPSO algorithm for three categories. The second part is used to enhance the current characteristics of small tributaries and extract the small tributaries. The first stage involves extracting water pixels from three tributaries via the DPSO algorithm, and the second step is to enhance the current characteristics of small tributaries and extract the small tributaries [41; 42]. The extraction of small tributaries from FY-3/MERSI images is mainly based on [PERSON]'s work [43]. [PERSON] proposed a Duda Road Operator (DRO) in his study to identify rural roads from low-resolution remote sensing data by locating roads less than or equal to three pixels wide. The DRO was further studied and extended to extract natural features, such as coastlines and valleys. [PERSON]'s research involves evaluating images pixel by pixel based on a set of operators, each of which measures the local image attributes through a pixel and returns a score indicating the likelihood that the pixel is a road [44; 45; 46]. The higher the calculated value of the operator, the more likely it is a road pixel. In the FY-3/MERSI images, small tributaries showed as less than or equal to 3-pixel values, so the DRO operator was introduced to enhance the linear features and extract small tributaries. Rivers vary greatly in morphology due to their different points of origin. Rivers formed on mountains tend to be very tortuous in shape along with the direction of valleys. However, rivers in flat areas and artificially excavated rivers tend to be squarer in shape. Considering the effect of river shape on the enhancement of linear features, only the central pixel of the river and the pixels on both  
Figure 8: The flow chart of SMDPSO + linear feature enhancement (LFE). Figure 7: Probability map of the tributary water bodies: (**a**) water probability of tributaries; (**b**) probability curve of tributary water bodies. sides of the central pixel in different directions are considered in the process of introducing the DRO operator. Based on an image with an inconsistent width of river water pixels, \(3\times 3\) and \(5\times 5\) pixels were used to enhance the linear features of the image. Considering that the water probability value of the surface water pixel is larger than the rest of the pixel, to avoid the influence of other pixels, only the cases where the water probability value of the pixel in the center is larger than the two sides were calculated. The DRO operators include four operators in different directions to calculate the linear enhancement results for operators in different directions and determine the maximum values for \(3\times 3\) and \(5\times 5\) pixels in different directions as the final linear feature enhancement result values.