Abstract
Airborne Laser Scanning (ALS) point cloud classification is a critical task in remote sensing and photogrammetry communities, which can be widely utilized in urban management, powerline surveying and forest monitoring, etc. In particular, the characteristics of ALS point clouds are distinctive in three aspects, (1) numerous geometric instances (e.g. tracts of roofs); (2) extreme scale variations between different categories (e.g. car v.s. roof); (3) discrepancy distribution along the elevation, which should be specifically focused on for ALS point cloud classification. In this paper, we propose a geometry-attentional network consisting of geometry-aware convolution, dense hierarchical architecture and elevation-attention module to embed the three characteristics effectively, which can be trained in an end-to-end manner. Evaluated on the ISPRS Vaihingen 3D Semantic Labeling benchmark, our method achieves the state-of-the-art performance in terms of average F1 score and overall accuracy (OA). Additionally, without retraining, our model trained on the above Vaihingen 3D dataset can also achieve a better result on the dataset of 2019 IEEE GRSS Data Fusion Contest 3D point cloud classification challenge (DFC 3D) than the baseline (i.e. PointSIFT), which verifies the stronger generalization ability of our model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: ISPRS Journal of Photogrammetry and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.