Remote sensing tuning: A survey

Dongshuo Yin, Ting-Feng Zhao, Deng-Ping Fan, Shutao Li, Bo Du, Xian Sun, Shi-min Hu

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 revolution in this field. Fine-tuning techniques serve as a bridge between remote sensing downstream tasks and advanced foundation models. As RSFMs become 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 methods 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 provides nine areas worth exploring in this field. Remote sensing fine-tuning methods in this survey can be found on \href{https://github.com/DongshuoYin/Remote-Sensing-Tuning-A-Survey}{https://github.com/DongshuoYin/Remote-Sensing-Tuning-A-Survey