Next Article in Journal
Collection and Integration of Local Knowledge and Experience through a Collective Spatial Analysis
Previous Article in Journal
Using Remote Sensing Products to Identify Marine Association Patterns in Factors Relating to ENSO in the Pacific Ocean
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle

A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

1,*, 2,* and 3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
School of Resources and Environment Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Authors to whom correspondence should be addressed.
Academic Editors: Stefan Leyk and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(1), 30;
Received: 17 May 2016 / Revised: 25 November 2016 / Accepted: 15 January 2017 / Published: 23 January 2017
PDF [6511 KB, uploaded 23 January 2017]


In the fields of geographic information systems (GIS) and remote sensing (RS), the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC), is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters”) if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm. View Full-Text
Keywords: spatial clustering; convex hull retraction; multi-density point cluster; CDHC spatial clustering; convex hull retraction; multi-density point cluster; CDHC

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).

Share & Cite This Article

MDPI and ACS Style

Li, S.; Li, W.; Qiu, J. A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis. ISPRS Int. J. Geo-Inf. 2017, 6, 30.

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



[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