Enhanced Autocorrelation-Based Algorithms for Filtering Airborne Lidar Data over Urban Areas

Abstract

© 2015 American Society of Civil Engineers. Many existing algorithms for light detection and ranging (lidar) data classification are known to perform reliably; however, the automation of the classification of complex urban scenes is still a challenging problem. In this paper, two classification algorithms based on spatial autocorrelation statistics, such as the Local Moran's I and the Getis-Ord Gi*, are proposed. These autocorrelation statistics are computed over sample urban areas, including complex terrain with diverse building characteristics. The proposed autocorrelation-based algorithms are applied to airborne lidar point clouds over the complex urban areas to generate highly accurate digital elevation models (DEMs) and classify the lidar points as ground and nonground points by using the DEMs. It is also demonstrated that the minimum-based rasterization and slope-based filtering can be integrated to effectively remove outliers from the DEMs. The test results showed that the autocorrelation-based algorithms produce high-level assessment of overall classification accuracy and Cohen's kappa index as well as a low level of total errors in complex urban scenes.