Adaptive Multiscale Feature Extraction in a Distributed System for Semantic Classification of Airborne LiDAR Point Clouds

Abstract

Multiple spatial scales have been used extensively for feature extraction from light detection and ranging (LiDAR) point clouds. These features have been used for semantic classification, segmentation, and other data analysis methods. There is a gap in the adaptive methodology for the effective use of multiple scales here. This stems from determining the best strategy to aggregate the information or features gathered from different scales. The widely used multiscale method is feature extraction at an optimal scale, which is in itself an adaptive method. However, the success of identifying the optimal scale depends on the set of scales used in its determination, as it must include the scale where the global minimum of eigenentropy occurs. An alternative method is to average features across multiple scales, which works in specific scenarios. In order to improve the flexibility of using different methods in the same workflow, we propose an adaptive method for the selection of multiscale feature extraction for semantic classification of LiDAR point clouds, with a focus on airborne laser scans. Our decision-making process for finding the best multiscale method exploits spatial locality of the features. We show how such a control strategy can be implemented in an Apache Spark–Cassandra distributed system for processing large-scale point clouds using voxelization for preserving spatial locality, and binomial logistic regression for selecting voxels to implement a specific multiscale method at. Our results show significant improvement in classification accuracy in the Dayton Annotated Laser Earth Scan (DALES) data, implemented using Spark MLlib in our distributed system.