Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks

Abstract

Various classification methods have been developed to extract meaningful information from Airborne Laser Scanner (ALS) point clouds. However, the accuracy and the computational efficiency of the existing methods need to be improved, especially for the analysis of large datasets (e.g., at regional or national levels). In this paper, we present a novel deep learning approach to ground classification for Digital Terrain Model (DTM) extraction as well as for multi-class land-cover classification, delivering highly accurate classification results in a computationally efficient manner. Considering the top-down acquisition angle of ALS data, the point cloud is initially projected on the horizontal plane and converted into a multi-dimensional image. Then, classification techniques based on Fully Convolutional Networks (FCN) with dilated kernels are designed to perform pixel-wise image classification. Finally, labels are transferred from pixels to the original ALS points. We also designed a Multi-Scale FCN (MS-FCN) architecture to minimize the loss of information during the point-to-image conversion. In the ground classification experiment, we compared our method to a Convolutional Neural Network (CNN)-based method and LAStools software. We obtained a lower total error on both the International Society for Photogrammetry and Remote Sensing (ISPRS) filter test benchmark dataset and AHN-3 dataset in the Netherlands. In the multi-class classification experiment, our method resulted in higher precision and recall values compared to the traditional machine learning technique using Random Forest (RF); it accurately detected small buildings. The FCN achieved precision and recall values of 0.93 and 0.94 when RF obtained 0.91 and 0.92, respectively. Moreover, our strategy significantly improved the computational efficiency of state-of-the-art CNN-based methods, reducing the point-to-image conversion time from 47 h to 36 min in our experiments on the ISPRS filter test dataset. Misclassification errors remained in situations that were not included in the training dataset, such as large buildings and bridges, or contained noisy measurements.As a result, points' features are represented by pixel values in the extracted image. Consequently, the point classification task is transformed to a pixel-wise image classification task. To address this task, we introduce a Fully Convolutional Network (FCN), which is a CNN variant that can predict the classification labels of every pixel in the image directly. We adopt FCN with dilated kernel (FCN-DK) for the classification [15]. FCN-DK is a no down-sampling network architecture that maintains the spatial size of the feature maps of each layer to be the same as the input. It uses dilated kernels to capture larger spatial contextual information, and therefore increases the receptive field of the network without increasing the number of parameters. We modify the FCN-DK network to perform ground and multi-class classification of an ALS point cloud. We also propose Multi-Scale FCN (MS-FCN) architectu...

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

“…The recall and precision value indicated that the model is highly trustable (precision: 0.86), correctly detecting 72% of the ground points. The values attained for the ground class were similar to the ones reported by Rizaldy et al [25], who reached an average total error of 5.21%, with low type I (4.28%) and type II (14.28%) errors, meaning that more non-ground points were mislabeled. The higher type II error was explained by the number of non-ground data points in both samples being considerably lower than the number of ground points.…”

“…The misclassification of some parts of the trees that were located near to the elevated embankment and the terrain break lines at the river banks produced the highest C2C distance (Figure 4a,c). Rizaldy et al [25] reported a similar problem with the misclassification of non-ground points in the area where the ground surface is connected to the elevated bridge, because the boundary between the ground and the bridge is fuzzy owing to the gradual inclination of the road surface. As tree parts classified as ground were located at the same height as embankment, a similar explanation could be applied.…”

“…The model, trained with 17 million labeled points, provided high accuracy. Rizaldy et al [25] used a fully convolutional network based approach to classify an ALS point cloud into ground, building, and vegetation with an average error of 5.21%. The original point cloud was converted into images by calculating each pixel value based on the features of the lowest point.…”

Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks

“…The last convolutional layer is a classification layer that produces two feature maps, which are the number of classes (AFB and non-AFB class), and they are fed to the soft-max module for the prediction as an output map. This architecture is inspired and improved with respect to [19,20].…”

Change Detection Between Digital Surface Models From Airborne Laser Scanning and Dense Image Matching Using Convolutional Neural Networks

“…In addition, CNN also shows superior performance in extracting distinctive features from point clouds (Hu and Yuan, 2016;Rizaldy et al, 2018). Our change detection framework was inspired by these two papers.…”