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EFFICIENT LARGE-SCALE AIRBORNE LIDAR DATA CLASSIFICATION VIA FULLY CONVOLUTIONAL NETWORK
| Content Provider | Scilit |
|---|---|
| Author | Maset, E. Padova, B. Fusiello, A. |
| Copyright Year | 2020 |
| Description | Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%. |
| Ending Page | 532 |
| Page Count | 6 |
| Starting Page | 527 |
| e-ISSN | 21949034 |
| DOI | 10.5194/isprs-archives-xliii-b3-2020-527-2020 |
| Journal | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume Number | XLIII-B3-2 |
| Language | English |
| Publisher | Copernicus GmbH |
| Publisher Date | 2020-08-21 |
| Access Restriction | Open |
| Subject Keyword | Imaging Science Remote Sensing Point Cloud Fully Convolutional Network Large Scale |
| Content Type | Text |
| Resource Type | Article |
