Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection

TL;DR

This paper introduces an unsupervised deep learning approach for 3D point cloud change detection that leverages self-supervised learning techniques, outperforming traditional methods without requiring labeled data.

Contribution

It presents a novel unsupervised method combining deep clustering and contrastive learning for 3D change detection, reducing reliance on annotated datasets.

Findings

Achieves over 85% mean accuracy in experiments.

Outperforms traditional unsupervised methods by about 9%.

Effective in scenarios lacking labeled training data.

Abstract

Change detection from traditional \added{2D} optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud \added{from photogrammetry or LiDAR surveying} can fill this gap by providing critical depth information. While most existing machine learning based 3D point cloud change detection methods are supervised, they severely depend on the availability of annotated training data, which is in practice a critical point. To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning. The proposed method also relies on an adaptation of deep change vector analysis to 3D point cloud via nearest point comparison. Experiments conducted on \added{an aerial LiDAR survey dataset} show that the proposed method obtains higher performance in comparison to the traditional unsupervised methods, with a gain of about 9\% in mean accuracy (to reach more than 85\%). Thus, it appears to be a relevant choice in scenario where prior knowledge (labels) is not ensured.