综述遥感图像掩码建模最新进展，助力云遮、超分等难题解决。
MIMRS: A Survey on Masked Image Modeling in Remote Sensing
- 系统梳理遥感中掩码图像建模方法与技术路径
- 覆盖云去除、多模态融合、超分辨率等核心应用
- 适合遥感、自监督学习研究者快速入门参考
掩码图像建模（Masked Image Modeling, MIM）是一种自监督学习技术，通过掩蔽图像的像素、图像块或潜在表示，并利用可见上下文训练模型预测缺失信息。该方法已成为自监督学习的核心，通过利用未标注数据进行预训练，推动视觉理解新可能。在遥感领域，MIM应对云遮、遮挡和传感器限制导致的数据不完整问题，支持云去除、多模态数据融合与超分辨率等应用。本文综述（MIMRS）首次全面梳理遥感中掩码图像建模的发展脉络，总结前沿方法、应用场景与未来方向，为该快速演进领域提供基础性参考。
原文摘要 · Abstract (English)
Masked Image Modeling (MIM) is a self-supervised learning technique that involves masking portions of an image, such as pixels, patches, or latent representations, and training models to predict the missing information using the visible context. This approach has emerged as a cornerstone in self-supervised learning, unlocking new possibilities in visual understanding by leveraging unannotated data for pre-training. In remote sensing, MIM addresses challenges such as incomplete data caused by cloud cover, occlusions, and sensor limitations, enabling applications like cloud removal, multi-modal data fusion, and super-resolution. By synthesizing and critically analyzing recent advancements, this survey (MIMRS) is a pioneering effort to chart the landscape of mask image modeling in remote sensing. We highlight state-of-the-art methodologies, applications, and future research directions, providing a foundational review to guide innovation in this rapidly evolving field.
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