The building contours, as one of the most important features for representing geometry, are widely used in various applications including urban modeling and reconstruction. Automatic extraction of high-fidelity compact contours from unstructured point clouds is rather challenging, and the existing methods are limited in generating global-aware and artifact-free building contours. Here, we approach contour extraction as a Bayesian inference problem. Two ideas are proposed to obtain building contour points and edges directly from unstructured point clouds. First, we construct a point-edge collaborative (PECo) Bayesian framework to couple the information of contour points and contour edges. The developed model fully takes local contour features, global edge structures, and global geometric priors into account. Second, given the Bayesian framework for building contouring, we leverage an expectation maximization (EM) algorithm to iteratively infer the contour edges in a maximum posteriori manner. The alternate EM iterations between point and edge domains progressively refine the local pointwise information to a global representation of contour edges. As a result, a synergistic effect between the point features and edge structures for global-aware building contouring is attained. Our approach outperforms the state-of-the-art methods in terms of geometric accuracy and structural compactness in modeling buildings with various complexities. Furthermore, the compactness of the contouring results can be more flexible and easily controlled.

To achieve high scene classification performance of high spatial resolution remote sensing images (HSR-RSIs), it is important to learn a discriminative space in which the distance metric can precisely measure both similarity and dissimilarity of features and labels between images. While the traditional metric learning methods focus on preserving interclass separability, label consistency (LC) is less involved, and this might degrade scene images classification accuracy. Aiming at considering intraclass compactness in HSR-RSIs, we propose a discriminative distance metric learning method with LC (DDML-LC). The DDML-LC starts from the dense scale invariant feature transformation features extracted from HSR-RSIs, and then uses spatial pyramid maximum pooling with sparse coding to encode the features. In the learning process, the intraclass compactness and interclass separability are enforced while the global and local LC after the feature transformation is constrained, leading to a joint optimization of feature manifold, distance metric, and label distribution. The learned metric space can scale to discriminate out-of-sample HSR-RSIs that do not appear in the metric learning process. Experimental results on three data sets demonstrate the superior performance of the DDML-LC over state-of-the-art techniques in HSR-RSI classification.

In 3D urban environments, features of interest (FOIs) are often occluded by clusters of buildings, which prevent a clear overview of important spatial features. State-of-the-art disocclusion methods for urban environments fall short of preserving cityscape appearance or require time-consuming computation. These methods use only one or two operators for disocclusion and might not strike a good balance between disocclusion and distortion control. We present a novel, automatic method enabling interactive context-aware visualization of urban features of interest, which combines four effective disocclusion operators including viewpoint elevation, road shifting, building scaling, and building displacement to disocclude the features of interest. Our method provides an optimum compromise among the disocclusion operators via an efficient constrained optimization and the post-polishing phrases, which minimizes the distortions while enforcing the visibility of the FOIs. The 3D views generated at interactive frame rates ensure a resemblance in the cityscape appearance to its original ones and provide a good overview of the FOIs. The experiments with real data demonstrate that our method can greatly facilitate tasks such as navigation, wayfinding, and information overlay.

This article presents a spatial cognition analysis technique for automated urban building clustering based on urban morphology and Gestalt theory. The proximity graph is selected to present the urban mrphology. The proximity graph considers the local adjacency among buildings, providing a large degree of freedom in object displacement and aggregation. Then, three principles of Gestalt theories, proximity, similarity, and common directions, are considered to extract potential Gestalt building clusters. Next, the Gestalt features are further characterized with seven indicators, that is, area difference, height difference, similarity difference, orientation difference, linear arrangement difference, interval difference, and oblique degree of arrangement. A support vector machine (SVM)-based approach is employed to extract the Gestalt building clusters. This approach transforms the Gestalt cluster extraction into a supervised discrimination process. The method presents a generalized approach for clustering buildings of a given street block into groups, while maintaining the spatial pattern and adjacency of buildings during the displacement operation. In applications of urban building generalization and three-dimensional (3D) urban panoramic-like view, the method presented in this article adequately preserves the spatial patterns, distributions, and arrangements of urban buildings. Moreover, the final 3D panoramic-like views ensure the accurate appearance of important features and landscapes.