Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection

In this paper, a novel recognition-driven variational framework, toward multiple building extraction from aerial and satellite images, is introduced. To this end, competing shape priors are considered, and building extraction is addressed through an image segmentation approach that involves the use of a data-driven term constrained from the prior models. The proposed framework extends previous approaches toward the integration of multiple shape priors into the level-set segmentation. In particular, it estimates the number of buildings as well as their pose from the observed data. Therefore, it can address multiple building extraction from a single optical image, a highly demanding task of fundamental importance in various geoscience and remote-sensing applications. Furthermore, it can be easily extended to deal with other remote-sensing data through a simple modification of the image term. Very promising experimental results and the performed qualitative and quantitative evaluation demonstrate the potential of our approach.