Shape Similarity Assessment Method for Coastline Generalization
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 bila...
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doaj-9ec3eb8feeb342d8abe02c91c0119af22020-11-25T01:03:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-07-017728310.3390/ijgi7070283ijgi7070283Shape Similarity Assessment Method for Coastline GeneralizationZhaoxing Li0Jingsheng Zhai1Fang Wu2Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450000, ChinaSchool of Marine Science and Technology, Tianjin University, Tianjin 300072, ChinaZhengzhou Institute of Surveying and Mapping, Zhengzhou 450000, ChinaAlthough 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.http://www.mdpi.com/2220-9964/7/7/283coastlinegeneralization qualityshape similarityconstrained Delaunay trianglebend |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhaoxing Li Jingsheng Zhai Fang Wu |
spellingShingle |
Zhaoxing Li Jingsheng Zhai Fang Wu Shape Similarity Assessment Method for Coastline Generalization ISPRS International Journal of Geo-Information coastline generalization quality shape similarity constrained Delaunay triangle bend |
author_facet |
Zhaoxing Li Jingsheng Zhai Fang Wu |
author_sort |
Zhaoxing Li |
title |
Shape Similarity Assessment Method for Coastline Generalization |
title_short |
Shape Similarity Assessment Method for Coastline Generalization |
title_full |
Shape Similarity Assessment Method for Coastline Generalization |
title_fullStr |
Shape Similarity Assessment Method for Coastline Generalization |
title_full_unstemmed |
Shape Similarity Assessment Method for Coastline Generalization |
title_sort |
shape similarity assessment method for coastline generalization |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2018-07-01 |
description |
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. |
topic |
coastline generalization quality shape similarity constrained Delaunay triangle bend |
url |
http://www.mdpi.com/2220-9964/7/7/283 |
work_keys_str_mv |
AT zhaoxingli shapesimilarityassessmentmethodforcoastlinegeneralization AT jingshengzhai shapesimilarityassessmentmethodforcoastlinegeneralization AT fangwu shapesimilarityassessmentmethodforcoastlinegeneralization |
_version_ |
1725201610875863040 |