| Autor: |
Yanchao Lian, Tuo Feng, Jinliu Zhou, Meixia Jia, Aijin Li, Zhaoyang Wu, Licheng Jiao, Myron Brown, Gregory Hager, Naoto Yokoya, Ronny Hansch, Bertrand Le Saux |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1158-1170 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2020.3035274 |
| Popis: |
We present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest included challenges with large-scale datasets for semantic 3-D reconstruction from satellite images and also semantic 3-D point cloud classification from airborne LiDAR. 3-D reconstruction results are discussed separately in Part-A. In this Part-B, we report the results of the two best-performing approaches for 3-D point cloud classification. Both are deep learning methods that improve upon the PointSIFT model with mechanisms to combine multiscale features and task-specific postprocessing to refine model outputs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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