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Sensors 2016, 16(6), 932;

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

Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
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)
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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

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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.

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