| Autor: |
Licheng Jiao, Zhongjian Huang, Xiaoqiang Lu, Xu Liu, Yuting Yang, Jiaxuan Zhao, Jinyue Zhang, Biao Hou, Shuyuan Yang, Fang Liu, Wenping Ma, Lingling Li, Xiangrong Zhang, Puhua Chen, Zhixi Feng, Xu Tang, Yuwei Guo, Dou Quan, Shuang Wang, Weibin Li, Jing Bai, Yangyang Li, Ronghua Shang, Jie Feng |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 10084-10120 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2023.3316302 |
| Popis: |
The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over 30 FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than 10 datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various FMs and confirm that pretrained large-scale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize 12 open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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