Next Article in Journal
Addressing Public Law Restrictions within a 3D Cadastral Context
Previous Article in Journal
Towards a Planetary Spatial Data Infrastructure
Article Menu
Issue 6 (June) cover image

Export Article

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

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(6), 184; doi:10.3390/ijgi6060184

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
Received: 21 April 2017 / Revised: 18 June 2017 / Accepted: 20 June 2017 / Published: 21 June 2017
View Full-Text   |   Download PDF [3532 KB, uploaded 23 June 2017]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top