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Morphing Linear Features Based on Their Entire Structures
Author(s) -
Deng Min,
Peng Dongliang
Publication year - 2015
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12111
Subject(s) - morphing , linear interpolation , delaunay triangulation , constrained delaunay triangulation , matching (statistics) , triangulation , generalization , interpolation (computer graphics) , algorithm , binary tree , line (geometry) , mathematics , binary number , combinatorics , linear programming , computer science , geometry , artificial intelligence , pattern recognition (psychology) , image (mathematics) , mathematical analysis , statistics , arithmetic
In this article, a new morphing method is proposed for two linear features at different scales, based on their entire structures ( MLBES in abbreviation). First, the bend structures of the linear features are identified by using a constrained Delaunay triangulation ( CDT in abbreviation) model and represented by binary bend‐structure trees. By matching the independent bends represented by the bend‐structure trees, corresponding independent bends are obtained. These corresponding independent bends are further used to match their child bends based on hierarchical bend structures so that corresponding bends are obtained. On this basis, the two linear features are split into pairs of corresponding subpolylines by the start and end points of the corresponding bends. Second, structures of the corresponding subpolylines are identified by the D ouglas‐ P eucker algorithm and represented by binary line generalization trees ( BLG ‐trees in abbreviation). The corresponding subpolylines are split into smaller corresponding subpolylines by matching the nodes of the BLG ‐trees. Third, the corresponding points are identified by using the linear interpolation algorithm for every pair of corresponding subpolylines. Finally, straight‐line trajectories are employed to generate a family of intermediate‐scale linear features. By comparison with other methods, it is found that MLBES is accurate and efficient.