Next Article in Journal
FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
Previous Article in Journal
An Ultrahigh Frequency Partial Discharge Signal De-Noising Method Based on a Generalized S-Transform and Module Time-Frequency Matrix
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(6), 932; doi:10.3390/s16060932

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

1
Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
2
Institute of Geodesy and Geoinformation Science (IGG), Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 20 March 2016 / Revised: 10 June 2016 / Accepted: 15 June 2016 / Published: 22 June 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [7801 KB, uploaded 22 June 2016]   |  

Abstract

A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process. View Full-Text
Keywords: registration; 3D building models; aerial imagery; geometric hashing; model to image matching registration; 3D building models; aerial imagery; geometric hashing; model to image matching
Figures

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

Jung, J.; Sohn, G.; Bang, K.; Wichmann, A.; Armenakis, C.; Kada, M. Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing. Sensors 2016, 16, 932.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top