Improved Building Detection Using Texture Information

The performance of automatic building detection techniques can be significantly impeded due to the presence of same-height objects, for example, trees. Consequently, if a building detection technique cannot distinguish between trees and buildings, both its false positive and false negative rates rise significantly. This paper presents an improved automatic building detection technique that achieves more effective separation of buildings from trees. In addition to using traditional cues such as height, width and colour, the proposed improved detector uses texture information from both LIDAR and orthoimagery. Firstly, image entropy and colour information are jointly applied to remove easily distinguishable trees. Secondly, a voting procedure based on the neighbourhood information from both the image and LIDAR data is employed for further exclusion of trees. Finally, a rule-based procedure using the edge orientation histogram from the image is followed to eliminate false positive candidates. The improved detector has been tested on a number of scenes from three different test areas and it is shown that the algorithm performs well in complex scenes.