Automatic extraction of LIDAR data classification rules

Abstract

LIDAR (Light Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents an AdaBoost algorithm for the identification of rules for the classification of raw LIDAR data mainly as buildings, ground and vegetation. First raw data are filtered, interpolated over a grid and segmented. Then geometric and topological relationships among regions resulting from segmentation constitute the input to the tree-structured classification algorithm. Results obtained on data sets gathered over the town of Pavia (Italy) are compared with those obtained by a rule-based approach previously presented by the authors for the classification of the regions.