Automated Extraction of Building Outlines From Airborne Laser Scanning Point Clouds

Abstract

Automatic extraction of building outlines from airborne laser scanning (ALS) point clouds has been an active topic in the field of photogrammetry, remote sensing, and computer vision. In this letter, a marked point process method is implemented to extract building outlines from ALS point clouds. First, the Gibbs energy model of building objects is defined to describe the building points. Second, the defined Gibbs energy model is sampled within the framework of reversible-jump Markov chain Monte Carlo and optimized to find an optimal energy configuration by simulated annealing. Finally, the detected building objects are refined to eliminate false detections, and the outlines of buildings are derived from the detected building objects by morphological operators. The standard data set provided by ISPRS is used to verify the validity of the proposed method. The method extracted building objects from the standard data sets with an average completeness of 87.3% and correctness of 91.57% at the pixel level, and an average completeness of 77.6% (97.3%) and correctness of 98.1% (97.9%) at the object $(\hbox{object} > 50\ \hbox{m}^{2})$ level.