Abstract
In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. 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 co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.
Highlights
In recent years, a large number of mega cities provide detailed building models, representing their static environment for supporting critical decision for smart city applications
In model-to-image registration, most of registration methods are based on feature-based methods because models have no texture information while salient objects can be extracted from the models and image
To register a single image with existing 3D building models, edged corner features are extracted from both datasets and their corresponding matches are computed by an enhanced geometric hashing method
Summary
A large number of mega cities provide detailed building models, representing their static environment for supporting critical decision for smart city applications. Most of registration methods show promising success in a controlled environment, Zitova and Flusser (2003) pointed out that the registration is a challenging vision task due to the diverse nature of remote sensing data (resolution, accuracy, signal-to-noise ratio, spectral bands, scene complexity and occlusions). These variable affecting the performance of registration leads to severe difficulty of its generalization. An alternative method is to use known points instead of direct survey of GCPs. Nowadays, large-scale 3D building models have been generated over the major cities of the world. We rectify the method by introducing several constraints and geometric properties of context feature because a standard geometric hashing method has its own limitations
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