Surface Networks: New techniques for their automated extraction, generalisation and application
by sanjay_rana on May 22, 2010
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The nature of data structures used for the representation of terrain has a great influence on the possible applications and reliability of consequent terrain analyses....
The nature of data structures used for the representation of terrain has a great influence on the possible applications and reliability of consequent terrain analyses.
This research demonstrates a concise review and treatment of the surface network data structure, a topological data structure for terrains. A surface network represents the terrain as a graph where the vertices are the fundamental topographic features (also called critical points), namely the local peaks, pits, passes (saddles) and the edges are the ridges and channels that link these vertices. Despite their obvious and widely believed potential for being a natural and intuitive representation of terrain datasets, surface networks have only attracted limited research, leaving several unsolved aspects, which have restricted their use as viable digital terrain data structures. The research presented here presents novel techniques for the automated generation, analysis and application of surface networks.
The research reports a novel method for generating the surface networks by extending the ridges and channels, unlike the conventional critical points-based approach. This proposed algorithm allows incorporation of much wider variety of terrain features in the surface network data structure.
Several ways of characterising terrain structure based on the graph-theoretic analysis of surface networks are presented. It is shown that terrain structures display certain empirical characteristics such as the stability of the structure under changes and relationship between hierarchies of topographic features. Previous proposals for the simplification of surface networks have been evaluated for potential limitations and solutions have been presented including a user-defined simplification. Also methods to refine (to add more details to) a surface network have been shown.
Finally, it is shown how surface networks can be successfully used for spatial analyses e.g. optimisation of visibility index computation time, augmenting the visualisation of dynamic raster surface animation, and generating multi-scale morphologically consistent terrain generalisations.
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