| Autor: |
Xian Sun, Peijin Wang, Zhiyuan Yan, Wenhui Diao, Xiaonan Lu, Zhujun Yang, Yidan Zhang, Deliang Xiang, Chen Yan, Jie Guo, Bo Dang, Wei Wei, Feng Xu, Cheng Wang, Ronny Hansch, Martin Weinmann, Naoto Yokoya, Kun Fu |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8922-8940 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2021.3106941 |
| Popis: |
In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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