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黄丽娜的论文在ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 刊出
发布时间:2017-09-01 15:55:15 发布者:yz 浏览次数:

标题:A Matrix-Based Structure for Vario-Scale Vector Representation over a Wide Range of Map Scales: The Case of River Network Data

作者:Huang, LN (Huang, Lina); Ai, TH (Ai, Tinghua); Van Oosterom, P (Van Oosterom, Peter); Yan, XF (Yan, Xiongfeng); Yang, M (Yang, Min)

来源出版物:ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION卷:6期:7 文献编号: 218 DOI:来源出版物:ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 卷:6期:7 文献编号: 212 出版年:JUL 2017

摘要:The representation of vector data at variable scales has been widely applied in geographic information systems and map-based services. When the scale changes across a wide range, a complex generalization that involves multiple operations is required to transform the data. To present such complex generalization, we proposed a matrix model to combine different generalization operations into an integration. This study was carried on a set of river network data, where two operations, i.e., network pruning accompanied with river simplification, were hierarchically constructed as the rows and columns of a matrix. The correspondence between generalization operations and scale, and the scale linkage of multiple operations were also explicitly defined. In addition, we developed a vario-scale data structure to store the generalized river network data based on the proposed matrix. The matrix model was validated and assessed by a comparison with traditional methods that conduct generalization operations in sequence. It was shown that the matrix model enabled complex generalization with good generalization quality. Taking advantage of the corresponding vario-scale data structure, the river network data could be obtained at any arbitrary scale, and the vario-scale representation was achieved across a wide scale range.

入藏号:WOS:000407506900035

文献类型:Article

语种:English

作者关键词: matrix model; vario-scale representation; complex generalization; hydrographic network generalization

扩展关键词: LINE GENERALIZATION; SIMPLIFICATION; ALGORITHMS; FEATURES; ZOOM

通讯作者地址:Ai, TH (通讯作者),Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址:linahuang@whu.edu.cn; tinghuaai@whu.edu.cn; p.j.m.vanoosterom@tudelft.nl; xiongfeng.yan@whu.edu.cn; yangmin2003@whu.edu.cn

地址:

[Huang, Lina; Ai, Tinghua; Yan, Xiongfeng; Yang, Min] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Van Oosterom, Peter] Delft Univ Technol, GIS Technol, OTB Res, Fac Architecture & Built Environm, Julianalaan 134, NL-2628 BL Delft, Netherlands.

研究方向:Physical Geography; Remote Sensing

ISSN:2220-9964

影响因子:0.371

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