Object-based level set model for building detection in urban area

This paper studies an new approach to creating a variational level set model for buildings detection by combining LiDAR point clouds and Aerial image data. The level set model introduces an object-based image analysis technique. Firstly, a fundamental object-based level set framework is built by neighbor analysis of remote sensing image. Then, several derived products directly or indirectly from LiDAR raw point cloud data, like nDSM and absolute roughness data, are used to construct a novel energy term in relation to height and roughness of non-terrain objects, in order to make up the disadvantages caused by insufficient information only from remote sensing image. Thus, a closely combined model for buildings extraction has formed. The model can well fuse spectral feature, height and roughness information of objects from different sensors. Finally, experiments on pairs of Aerial image and LiDAR 3D point cloud data are carried out, and conclusions can be drawn that our model can effectively separate various small or high building in urban area from other land covers, including trees, grass, ground etc., and alleviate those influence caused by shadow, occlusions or spectral inhomogeneity.