Feature Conjugation for Intensity-Coded LIDAR Point Clouds

Abstract

Feature conjugation is a major task in modern-day spatial analysis and contributes to efficient integration across multiple data sets. In this study, an efficient approach that utilizes the intensity information provided in most light detection and ranging (LIDAR) data sets for feature conjugation is proposed. First, a two-dimensional (2D) intensity map is generated based on the original intensity-coded LIDAR observables in three-dimensional (3D) space. The 2D map is further transformed into a regularly sampled image, and an image feature detection technique is subsequently applied to identify point conjugations between a pair of intensity maps. Finally, the paired conjugations in the image space are mapped backward into the LIDAR space, and the object coordinates of the conjugate points can be verified and obtained. Based on the numerical results from a real world case study, it is illustrated that by fully exploring the existing spectral information, a reliable feature conjugation across multiple LIDAR data sets can be easily achieved in an efficient and automatic manner.