Abstract
Although shape similarity is one fundamental element in coastline generalization quality, its related research is still inadequate. Consistent with the hierarchical pattern of shape recognition, the Dual-side Bend Forest Shape Representation Model is presented by reorganizing the coastline into bilateral bend forests, which are made of continuous root-bends based on Constrained Delaunay Triangulation and Convex Hull. Subsequently, the shape contribution ratio of each level in the model is expressed by its area distribution in the model. Then, the shape similarity assessment is conducted on the model in a top–down layer by layer pattern. Contrast experiments are conducted among the presented method and the Length Ratio, Hausdorff Distance and Turning Function, showing the improvements of the presented method over the others, including (1) the hierarchical shape representation model can distinguish shape features of different layers on dual-side effectively, which is consistent with shape recognition, (2) its usability and stability among coastlines and scales, and (3) it is sensitive to changes in main shape features caused by coastline generalization.
Highlights
Coastline is the dynamic boundary between the land and ocean, which is highly related to territorial sea sovereignty, maritime transport, marine resource development, marine science examination, etc
Shape similarity assessment is of great importance in quality assessment of coastline generalization
Coastlines are divided into a pair of bend sequences by using the Constrained Delaunay Triangulation (CDT) and Convex Hull theory
Summary
Coastline is the dynamic boundary between the land and ocean, which is highly related to territorial sea sovereignty, maritime transport, marine resource development, marine science examination, etc. The formation of coastline received a complex effect of many factors such as tides, waves, ocean currents and biological activities, in addition to the general Earth surface processes, making its shape rather irregular and complicated. In the field of cartography, coastline is usually defined as the boundary reached by average high tide line, which means it belongs to linear features. To fill the multi-scale representation needs [1,2,3], fine-grained coastlines must be transformed into coarse-grained coastline features. The need for coastline generalization is inevitable. There have been presented a lot of ‘automatic’ linear feature generalization methods in the past decades, such as the Douglas–Peucker algorithm [4], the Li–Openshaw algorithm [5], and the Snake model [6,7]
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