期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2021
卷号：V-2-2021
页码：27-34
DOI：10.5194/isprs-annals-V-2-2021-27-2021
语种：English
出版社：Copernicus Publications
摘要：Railroad environments are peculiar, as they combine dense urban areas, along with rural parts. They also display a very specific spatial organization. In order to monitor a railway network a at country scale, LiDAR sensors can be equipped on a running train, performing a full acquisition of the network. Then most processing steps are manually done. In this paper, we propose to improve performances and production flow by creating a classification of the acquired data. However, there exists no public benchmark, and little work on LiDAR data classification in railroad environments. Thus, we propose a weakly supervised method for the pointwise classification of such data. We show that our method can be improved by using the il/isub0/sub-cut pursuit algorithm and regularize the noisy pointwise classification on the produced segmentation. As production is envisaged in our context, we designed our implementation such that it is computationally efficient. We evaluate our results against a manual classification, and show that our method can reach a FScore of 0.96 with just a few samples of each class.
