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Erratum published on 7 July 2017, see ISPRS Int. J. Geo-Inf. 2017, 6(7), 207.
Open AccessArticle

Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty

1
Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450052, China
2
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
3
School of Software Engineering, Tongji University, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(6), 184; https://doi.org/10.3390/ijgi6060184
Received: 21 April 2017 / Revised: 18 June 2017 / Accepted: 20 June 2017 / Published: 21 June 2017
This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas–Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment. View Full-Text
Keywords: linear feature; simplification; quality assessment; spatial uncertainty; hierarchical model linear feature; simplification; quality assessment; spatial uncertainty; hierarchical model
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Zhai, J.; Li, Z.; Wu, F.; Xie, H.; Zou, B. Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty. ISPRS Int. J. Geo-Inf. 2017, 6, 184.

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