The past few years have seen an accelerating integration of deep learning (DL) techniques into various remote sensing (RS) applications, highlighting their power to adapt and achieving unprecedented advancements. In the present review, we provide an exhaustive exploration of the DL approaches proposed specifically for the spatial downscaling of RS imagery. A key contribution of our work is the presentation of the major architectural components and models, metrics, and data sets available for this task as well as the construction of a compact taxonomy for navigating through the various methods. Furthermore, we analyze the limitations of the current modeling approaches and provide a brief discussion on promising directions for image enhancement, following the paradigm of general computer vision (CV) practitioners and researchers as a source of inspiration and constructive insight.

中文翻译：

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用于缩小遥感图像的深度学习：融合和超分辨率

在过去的几年里，深度学习 (DL) 技术加速集成到各种遥感 (RS) 应用中，突显了它们的适应能力并取得了前所未有的进步。在本综述中，我们对专门为 RS 图像的空间缩小而提出的 DL 方法进行了详尽的探索。我们工作的一个关键贡献是介绍了可用于此任务的主要架构组件和模型、指标和数据集，以及构建用于浏览各种方法的紧凑分类法。此外，我们分析了当前建模方法的局限性，并简要讨论了图像增强的有希望的方向，