Vision Foundation Models in Remote Sensing: A Survey
It provides a comprehensive overview for researchers and practitioners in remote sensing, addressing technical challenges and future directions, but is incremental as it synthesizes existing advancements rather than introducing new methods.
This survey paper reviews the application of vision foundation models in remote sensing, highlighting how these large-scale pre-trained models have improved accuracy and efficiency across various tasks, with self-supervised learning techniques like contrastive learning and masked autoencoders enhancing performance and robustness.
Artificial Intelligence (AI) technologies have profoundly transformed the field of remote sensing, revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, remote sensing research has been significantly enhanced by the advent of foundation models-large-scale, pre-trained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This paper provides a comprehensive survey of foundation models in the remote sensing domain. We categorize these models based on their architectures, pre-training datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those foundation models. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pre-training methods, particularly self-supervised learning techniques like contrastive learning and masked autoencoders, remarkably enhance the performance and robustness of foundation models. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for continued development and application of foundation models in remote sensing.