## 5 Discussion
However, the continuity of small tributaries needs to be improved. Mountain lakes in Thailand, Gandhi lake in India, Nasser reservoir in Egypt, and Congo river in the Congo are characterized by obvious surface waters due to their simple image types. The OA and Kappa coefficient values of the classification results of surface waters in these areas were both high, with an overall classification accuracy higher than 96.5% and Kappa coefficient values higher than 0.93. In Kazakhstan's Aral sea, Colombia's Magdalena river, Mexico's beach lakes, and in the Mississippi River, due to the complexity of ground type, shallow water and silt, sea tide, and cloud shadow, epicontinental water mixed with the rest of the class information; water extraction result complete degree is not high; OA is relatively low, at around 95%, and the Kappa coefficient is about 0.89. In terms of sensitivity, the sizes of the blocks increased from 3 to 5, and the overall classification accuracy gradually increased. With the increase in the block size, the greater distance limit of the block is solved, and the overall classification accuracy gradually decreases. Considering the solution time for land surface water, when the size of the block is 4, there are 65,536 (\(2^{16}\)) possible solutions for land surface water in any block. When the block size is 5, there are 33,554,432 (\(2^{25}\)) kinds of surface water in any block. The number of iterations is multiplied. Therefore, to achieve the highest accuracy and meet the low time requirements, the optimal block size chosen in this paper is 4. Through an approximate curve analysis of the OA and Kappa coefficients and the number of iterations in SMDPSO, the correlation between the fitting relationships of the OA and the Kappa coefficients and the number of iterations and the logarithm function was higher than 0.84. With a gradual increase in the number of iterations, the distribution law of logarithmic growth was realized. With 0-1000 iterations, the overall classification accuracy and Kappa coefficient increased rapidly. The growth slowed in 1000-2000 iterations, and the overall classification accuracy and Kappa coefficient tended to be stable after 2000 iterations. Since a higher number of iterations means a longer surface water extraction time, the number of iterations for all images in this paper was set at 2500. This research focuses on the comparison of water extraction methods. Five traditional methods are already mature. On the basis of comparing these methods, this paper added SMDPSO and SMDPSO+LFE to conduct experiments on water extraction methods. In the experiment of this method, SMDPSO+LFE is considered to have better accuracy, stability, and sensitivity. The results show that the accuracies of the seven methods are all greater than 93%. However, from the perspective of overall accuracy, SMDPSO+LFE is 3% to 4% higher than the conventional 5 methods. Kappa coefficient is 0.11 to 0.06 higher. In water extraction, including many other classification models, the process of improving the accuracy is relatively slow, which mainly depends on the characteristics of the data. Even a small increase in accuracy is of great significance, especially in the extraction of river tributaries.