Next Article in Journal
The IMU/UWB Fusion Positioning Algorithm Based on a Particle Filter
Previous Article in Journal
Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction
Open AccessArticle

Continuous Scale Transformations of Linear Features Using Simulated Annealing-Based Morphing

1
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
2
College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(8), 242; https://doi.org/10.3390/ijgi6080242
Received: 14 June 2017 / Revised: 21 July 2017 / Accepted: 3 August 2017 / Published: 7 August 2017
This paper presents a new method for use in performing continuous scale transformations of linear features using Simulated Annealing-Based Morphing (SABM). This study addresses two key problems in the continuous generalization of linear features by morphing, specifically the detection of characteristic points and correspondence matching. First, an algorithm that performs robust detection of characteristic points is developed that is based on the Constrained Delaunay Triangulation (CDT) model. Then, an optimal problem is defined and solved to associate the characteristic points between a coarser representation and a finer representation. The algorithm decomposes the input shapes into several pairs of corresponding segments and uses the simulated annealing algorithm to find the optimal matching. Simple straight-line trajectories are used to define the movements between corresponding points. The experimental results show that the SABM method can be used for continuous generalization and generates smooth, natural and visually pleasing linear features with gradient effects. In contrast to linear interpolation, the SABM method uses the simulated annealing technique to optimize the correspondence between characteristic points. Moreover, it avoids interior distortions within intermediate shapes and preserves the geographical characteristics of the input shapes. View Full-Text
Keywords: morphing; simulated annealing; detection of characteristic points; matching of characteristic points; continuous generalization morphing; simulated annealing; detection of characteristic points; matching of characteristic points; continuous generalization
Show Figures

Figure 1

MDPI and ACS Style

Li, J.; Ai, T.; Liu, P.; Yang, M. Continuous Scale Transformations of Linear Features Using Simulated Annealing-Based Morphing. ISPRS Int. J. Geo-Inf. 2017, 6, 242. https://doi.org/10.3390/ijgi6080242

AMA Style

Li J, Ai T, Liu P, Yang M. Continuous Scale Transformations of Linear Features Using Simulated Annealing-Based Morphing. ISPRS International Journal of Geo-Information. 2017; 6(8):242. https://doi.org/10.3390/ijgi6080242

Chicago/Turabian Style

Li, Jingzhong; Ai, Tinghua; Liu, Pengcheng; Yang, Min. 2017. "Continuous Scale Transformations of Linear Features Using Simulated Annealing-Based Morphing" ISPRS Int. J. Geo-Inf. 6, no. 8: 242. https://doi.org/10.3390/ijgi6080242

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

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop