@article{Yin2025, 
author = {Dongshuo Yin and Ting-Fen Zhao and Deng-Ping Fan and Shutao Li and Bo Du and Xian Sun and Shi-Min Hu},
title = {Remote sensing tuning: A survey},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {5},
pages = {897-937},
keywords = {deep learning, remote sensing, pre-training, fine-tuning, foundation models},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450490},
doi = {10.26599/CVM.2025.9450490},
abstract = {Large models have accelerated the development of intelligent interpretation in remote sensing. Many remote sensing foundation models (RSFM) have emerged in recent years, sparking a new wave of deep learning in this field. Fine-tuning techniques serve as a bridge between remote sensing downstream tasks and advanced foundation models. As RSFMs become more powerful, fine-tuning techniques are expected to lead the next research frontier in numerous critical remote sensing applications. Advanced fine-tuning techniques can reduce the data and computational resource requirements during the downstream adaptation process. Current fine-tuning techniques for remote sensing are still in their early stages, leaving a large space for optimization and application. To elucidate the current development and future trends of remote sensing fine-tuning techniques, this survey offers a comprehensive overview of recent research. Specifically, this survey summarizes the applications and innovations of each work and categorizes recent remote sensing fine-tuning techniques into six types: adapter-based, prompt-based, reparameterization-based, hybrid methods, partial tuning, and improved tuning. In the final section, this survey suggests nine areas worth exploring in this field. Remote sensing fine-tuning methods in this survey can be found at https://github.com/DongshuoYin/Remote-Sensing-Tuning-A-Survey.}
}