Machine Learning in LiDAR 3D point clouds

TL;DR

This paper compares various feature engineering and dimension reduction techniques to improve the classification accuracy of LiDAR 3D point cloud data, demonstrating that context augmentation and PCA enhance performance.

Contribution

It presents a preliminary comparison study of feature engineering and dimension reduction methods for LiDAR point cloud classification, highlighting the benefits of context augmentation and PCA.

Findings

Context augmentation improves classification accuracy.

PCA-based dimension reduction enhances performance.

Combining feature engineering with PCA yields better results.

Abstract

LiDAR point clouds contain measurements of complicated natural scenes and can be used to update digital elevation models, glacial monitoring, detecting faults and measuring uplift detecting, forest inventory, detect shoreline and beach volume changes, landslide risk analysis, habitat mapping, and urban development, among others. A very important application is the classification of the 3D cloud into elementary classes. For example, it can be used to differentiate between vegetation, man-made structures, and water. Our goal is to present a preliminary comparison study for the classification of 3D point cloud LiDAR data that includes several types of feature engineering. In particular, we demonstrate that providing context by augmenting each point in the LiDAR point cloud with information about its neighboring points can improve the performance of downstream learning algorithms. We also experiment with several dimension reduction strategies, ranging from Principal Component Analysis (PCA) to neural network-based auto-encoders, and demonstrate how they affect classification performance in LiDAR point clouds. For instance, we observe that combining feature engineering with a dimension reduction a method such as PCA, there is an improvement in the accuracy of the classification with respect to doing a straightforward classification with the raw data.