Abstract:In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed 'occlusions of random textures model' are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images.

This article proposes a 'dynamic' artificial bee colony (D-ABC) algorithm for solving optimizing problems. It overcomes the poor performance of artificial bee colony (ABC) algorithm, when applied to multi-parameters optimization. A dynamic 'activity' factor is introduced to D-ABC algorithm to speed up convergence and improve the quality of solution. This D-ABC algorithm is employed for multi-parameters optimization of support vector machine (SVM)-based soft-margin classifier. Parameter optimization is significant to improve classification performance of SVM-based classifier. Classification accuracy is defined as the objection function, and the many parameters, including 'kernel parameter', 'cost factor', etc., form a solution vector to be optimized. Experiments demonstrate that D-ABC algorithm has better performance than traditional methods for this optimizing problem, and better parameters of SVM are obtained which lead to higher classification accuracy.

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