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Proceedings Paper

Study on multiscale generalization of DEM based on lifting scheme
Author(s): Zuqiao Yang; HuanBin Liu; Nai Yang; XiaoHong Xiao
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Paper Abstract

Automatic generalization of geographical information is the core content of multi-scale representation of spatial data, but the scale dependent generalization methods are far from abundance because of its extreme complicacy. Most existing algorithms about automatic generalization do not relate to scale directly or accurately, not forecast and control the generalized effects, and cannot assess the global consistency of the generalized results. The rational and quantitative methods and criterions of measuring the extent of generalization have not still been sought out. Lifting Scheme is a new branch of Wavelet analysis burgeoning in last decades. It has several noteworthy aspects comparing with the Binary wavelet Transformation. The fundamentals of Lifting Scheme and the three constructing steps, which include Split step, Predict step and Merge step, are presented detailed in this paper. DEM can be represented in multi-scale model by the methods of The Lifting Scheme and The Binary Wavelet Transform. Compare with two methods, the Lifting Scheme has several superiorities by analyzing the experimental results: Firstly, the trend of relief could be preserved in course of transforming; secondly, the Lifting Scheme can process the points of boundary of DEM efficiently and the spatial data precision can also be maintained, and at last the calculation process of Lifting Scheme is more speedy.

Paper Details

Date Published: 16 October 2009
PDF: 6 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74922E (16 October 2009); doi: 10.1117/12.838611
Show Author Affiliations
Zuqiao Yang, Huanggang Normal Univ. (China)
Wuhan Univ. (China)
HuanBin Liu, Huanggang Normal Univ. (China)
Nai Yang, Wuhan Univ. (China)
XiaoHong Xiao, Huanggang Normal Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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