Adaptive Building Edge Detection by Combining Lidar Data and Aerial Images

The building edge detection plays a key role during building extraction, which is important and necessary for building description. The edges detected from aerial images have high horizontal accuracy and represent various edge shapes well. But the edge detection in images is often influenced by contrast, illumination and occlusion. LIDAR data are suitable for judging building regions, but miss some edge points due to the laser pulse discontinuousness. In order to make full use of the complementary advantages of the two data sources, a new adaptive method of building edge detection by combining LIDAR data and aerial images is proposed in this paper. Firstly, the objects and ground are separated by a filter based on morphological gradient. The non-building objects are removed by mathematical morphology and region growing. Secondly, the aerial image is smoothed by Gaussian convolution, and the gradients of the image are calculated. Finally, the edge buffer areas are created in image space by the edge points of the individual roof patch. The pixels with local maximal gradient in the buffer area are judged as the candidate edge. The ultimate edges are determined through fusing the edges in image and the roof patch by morphological operation. The experimental results show that the method is adaptive for various building shapes. The ultimate edges are closed and thin with one-pixel width, which are very suitable for subsequent building modelling.