A line-based spectral clustering method for efficient planar structure extraction from LiDAR data

Abstract

Planar structures are essential components of the urban landscape and automated extraction planar structure from LiDAR data is a fundamental step in solving complex mapping tasks such as building recognition and urban modelling. This paper presents a new and effective method for planar structure extraction from airborne LiDAR data based on spectral clustering of straight line segments. The straight line segments are derived from LiDAR scan lines using an Iterative-End-Point-Fit simplification algorithm. Adjacency matrix is then formed based on pair-wise similarity of the extracted line segments, and a symmetric affine matrix is derived which is then decomposed into eigenspace. The planar structures are then detected by mean-shift clustering algorithm in eigenspace. The use of straight line segments facilitates the processing and significantly reduces the computational load. Spectral analysis of straight line segments in eigenspace makes the planar structures more prominent, resulting in a robust extraction of planar surfaces. Experiments are performed on the ISPRS benchmark LiDAR data over three test sites containing a variety of buildings with complex roof structures and varying sizes. The experimental results, which are quantitatively evaluated independently by the ISPRS benchmark test group, are presented. The results show that the proposed method achieves on average 80% of completeness with over 98% of correctness. Better performance is observed over larger size of buildings (>10m2) with over 92% of completeness and nearly 100% of correctness in all test areas, indicating the robustness and high reliability of the proposed algorithm.