Using Training Samples Retrieved from a Topographic Map and Unsupervised Segmentation for the Classification of Airborne Laser Scanning Data

Abstract

The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1 score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1 score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1 score of 74.8% for ALS data classification while using the ground truth training samples the average F1 score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs.

Multi-View and Shift Rasterization Algorithm (MVSR) for Effective Identification of Ground in Dense Point Clouds

“…The remaining group of unclassified filters can include, for example, a hybrid filter [32], cloth simulation filter [33], multiclass classification filter using a convolutional network [34,35], or a combination of a cloth simulation filter with progressive TIN densification [36]. Algorithms based on deep learning (neural networks) have also been investigated (e.g., [37][38][39][40][41][42]), but these need to be trained on specific data and are, therefore, probably not yet widely used.…”

Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method

“…Another major challenge of designing deep learning systems for spatial-spectral data classification is the lack of labeled training samples [26]. Yang et al [27] propose automatic training sample generation using a 2D topographic map and an unsupervised segmentation by first separating ground from nonground points and then performing a point-in-polygon operation. Unsupervised segmentation was performed to reduce noise and improve accuracy of the previous task.…”

Untitled

“…В [Yang, 2020] предложена методика классификации данных ВЛС, согласно которой массив разбивается на классы посредством применения автоматического алгоритма выделения точек земли и полигонов, сформированных с помощью топографической карты и в результате неконтролируемой сегментации ТЛО. По карте автоматически формируются обучающие выборки, а неконтролируемая сегментация применяется для уменьшения уровня шума и улучшения точности формирования обучающих выборок.…”