期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B3a
页码：271-278
出版社：Copernicus Publications
摘要：This work aims at extracting 2D buildings from a single high-resolution satellite image in densely built-up urban areas. The whole algorithm follows the hypothesis-verification strategy. The key contribution of this paper is the edge verification method of the hypoth- esis verification process, which can greatly improve the accuracy and precision of the building extraction results from complex scenes. To extract more accurate and precise edges, we construct a probabilistic model and an optimization frame. First, a probability being the optimal edge is given to each possible edge, then the constraints of any two possible edges are estimated based on a machine learning method and the image evidences, finally, these constraints and other prior knowledge are integrated into an optimization problem, by solving which these probabilities can be computed and the optimal edge can be selected. At last, we provide some experimental results on large and complex scenes that demonstrate the robustness and accuracy of our algorithm. Over the past decades, automatic detection and reconstruction of buildings from aerial or satellite images has been an active re- search topic in both computer vision and digital photogramme- try (M.Fradkin et al., 2001, Suveg and Vosselman, 2004). This is due to an increasing need for building detection and recon- struction in a variety of applications. Especially in cartography, a powerful automatic system can greatly reduce the effort needed to assemble a 2D or 3D digital map. Other applications of this technology include urban planning, GIS update, environmental monitoring and virtual & augmented reality
关键词：building extraction; high resolution;urban scene;hypothesis-verification; edge verification;Probabilistic model
