STATISTICAL BUILDING ROOF RECONSTRUCTION FROM WORLDVIEW-2 STEREO IMAGERY

Abstract

3D building reconstruction from point clouds is an active research topic in remote sensing, photogrammetry and computer vision. Most of the prior research has been done on 3D building reconstruction from LiDAR data which means high resolution and dense data. The interest of this work is 3D building reconstruction from Digital Surface Models (DSM) of stereo image matching of space borne satellite data which cover larger areas than LiDAR datasets in one data acquisition step and can be used also for remote regions. The challenging problem is the noise of this data because of low resolution and matching errors. In this paper, a top-down and bottom-up method is developed to find building roof models which exhibit the optimum fit to the point clouds of the DSM. In the bottom up step of this hybrid method, the building mask and roof components such as ridge lines are extracted. In addition, in order to reduce the computational complexity and search space, roofs are classified to pitched and flat roofs as well. Ridge lines are utilized to estimate the roof primitives from a building library such as width, length, positions and orientation. Thereafter, a topdown approach based on Markov Chain Monte Carlo and simulated annealing is applied to optimize roof parameters in an iterative manner by stochastic sampling and minimizing the average of Euclidean distance between point cloud and model surface as fitness function. Experiments are performed on two areas of Munich city which include three roof types (hipped, gable and flat roofs). The results show the efficiency of this method in even for this type of noisy datasets.