A Lake Selection Method Based on Dynamic Multi-Scale Clustering

Current lake selection methods mostly uses the form of selecting as a whole, and are difficult to take into account the attribute characteristics, distribution characteristics and topological characteristics of the lake. By analyzing and imitating the cognitive behavior and process of artificial lakes selection, this paper proposes a lake selection method based on dynamic multiscale clustering. First, we set the area threshold to select the lakes with large area, then select the “isolated” lake through the buffer, then utilize the dynamic multi-scale clustering to the lake group to divide into areas with different density, decide the selection numbers by square root law and adopt the different selection strategy for the different areas, among which lakes are selected according to the comprehensive evaluation of importance calculated by iterative principal component analysis in the lake group class with a large number of features until the number of lakes reaches the selection number. Contrast of experiments show that the method maintains the morphological structure and density contrast of the lake group effectively, under the premise of considering the importance.