Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

• Main Authors: Yong Li, Dong Chen, Xiance Du, Shaobo Xia, Yuliang Wang, Sheng Xu, Qiang Yang
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Remote Sensing
• Subjects: airborne laser-scanning; segmentation; cluster classification; neighborhood topology; higher-order potential; CRF optimization
• Online Access: https://www.mdpi.com/2072-4292/11/10/1248

This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster&#8217;s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster&#8217;s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of &#8220;m<inline-formula> <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula>&#8221; estimated by averaging all classes of the <inline-formula> <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula>-scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an &#8220;ablation study&#8221; strategy.