Extracting rooftops from remote sensing images using both top-down and bottom-up processes

摘要

Building rooftop extraction is one of the most challenging tasks in the field of remote sensing image analysis. Existing methods usually perform poorly due to the complexity of the object and background. In this letter, we propose a novel framework for rooftop localization and geometric structure recovery via combining the strength of top-down and bottom-up methods. Specifically, a novel energy function combining the region term, the shape term, and the penalty term is proposed to eliminate the effect of unclosed contours and other disturbances such as park lots and shadows. In order to take advantage of bottom-up cues, a new penalty term is proposed, in which the position is determined by the directional spatial relationship between building and its shadow, and the orientation of a possible rooftop is estimated by the spatial context. A simulated annealing (SA) algorithm is applied to optimizing the function, which is fused with Markov chain Monte Carlo (MCMC) technique, and special transition kernels are designed in order to achieve convergent extraction results and get rid of local minimum. Experiments on IKONOS images demonstrate the robustness and accuracy of our method.