Domain adaptation is a crucial and increasingly important task in remote sensing, aiming to transfer knowledge from a source domain a differently distributed target domain. It has broad applications across various real-world applications, including remote sensing element interpretation, ecological environment monitoring, and urban/rural planning. However, domain adaptation in remote sensing poses significant challenges due to differences in data, such as variations in ground sampling distance, imaging modes from various sensors, geographical landscapes, and environmental conditions. In recent years, deep learning has emerged as a powerful tool for feature representation and cross-domain knowledge transfer, leading to widespread adoption in remote sensing tasks. In this paper, we present a comprehensive survey of significant advancements in deep learning based domain adaptation for remote sensing. We first introduce the preliminary knowledge to clarify key concepts, mathematical notations, and the taxonomy of methodologies. We then organize existing algorithms from multiple perspectives, including task categorization, input mode, supervision paradigm, and algorithmic granularity, providing readers with a structured understanding of the field. Next, we review widely used datasets and summarize the performance of state-of-the-art methods to provide an overview of current progress. We also identify open challenges and potential directions to guide future research in domain adaptation for remote sensing. Compared to previous surveys, this work addresses a broader range of domain adaptation tasks in remote sensing, rather than concentrating on a few subfields. It also presents a systematic taxonomy, providing a more comprehensive and organized understanding of the field. As a whole, this survey can inspire the research community, foster understanding, and guide future work in the field.

翻译：领域自适应是遥感领域一项至关重要且日益重要的任务，旨在将知识从源域迁移到分布不同的目标域。它在各种实际应用中具有广泛用途，包括遥感要素解译、生态环境监测以及城乡规划。然而，由于数据差异，例如地面采样距离的变化、来自不同传感器的成像模式、地理景观以及环境条件的不同，遥感中的领域自适应带来了重大挑战。近年来，深度学习已成为特征表示和跨领域知识迁移的强大工具，从而在遥感任务中得到广泛采用。本文全面综述了基于深度学习的遥感领域自适应方面的重大进展。我们首先介绍预备知识，以阐明关键概念、数学符号和方法论的分类体系。然后，我们从多个角度组织现有算法，包括任务分类、输入模式、监督范式和算法粒度，为读者提供对该领域的结构化理解。接下来，我们回顾了广泛使用的数据集，并总结了最先进方法的性能，以概述当前进展。我们还指出了开放挑战和潜在方向，以指导遥感领域自适应的未来研究。与以往的综述相比，本文涵盖了遥感领域更广泛的领域自适应任务，而非集中于少数子领域。它还提出了一个系统的分类体系，提供了对该领域更全面、更有条理的理解。总体而言，本综述能够启发研究界，增进理解，并指导该领域的未来工作。