In the last decade, there has been a significant surge of interest in machine learning, primarily driven by advancements in deep learning (DL). DL has emerged as a powerful solution to address various challenges in numerous fields, including remote sensing (RS). Graph Deep Learning (GDL), a sub-field of DL, has recently gained increasing attention in the RS community. Tasks in RS requiring detailed information about the relationships between image/scene features are particularly well-suited for GDL. This study examines the notion of GDL and its recent developments in RS-related fields. An extensive survey of the current state-of-the-art in GDL is presented in this paper, with a specific emphasis on five established graph learning techniques: Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Recurrent Neural Networks (GRNNs), Graph Auto-encoders (GAEs), and Graph Generative Adversarial Networks (GGANs). A taxonomy is proposed based on the input data type (dynamic or static) or task being considered. Several promising research directions for GDL in RS are suggested in this paper to foster productive collaborations between the two domains. To the best of our knowledge, this study is the first to provide a comprehensive review that focuses on graph deep learning in remote sensing.

在过去的十年中，人们对机器学习的兴趣大幅增长，这主要是由深度学习(DL) 的进步推动的。深度学习已成为解决遥感 (RS) 等众多领域各种挑战的强大解决方案。图深度学习（GDL）是 DL 的一个子领域，最近在 RS 社区中受到越来越多的关注。RS 中需要有关图像/场景特征之间关系的详细信息的任务特别适合 GDL。本研究探讨了 GDL 的概念及其在 RS 相关领域的最新发展。本文对 GDL 的当前最新技术进行了广泛的调查，特别强调了五种已建立的图学习技术：图卷积网络（GCN）、图注意力网络（GAT）、图循环神经网络（GRNN）、图自动编码器（GAE）和图生成对抗网络（GGAN）。根据输入数据类型（动态或静态）或正在考虑的任务提出分类法。本文提出了 RS 中 GDL 的几个有前景的研究方向，以促进两个领域之间的富有成效的合作。据我们所知，这项研究是第一个针对遥感中的图深度学习进行全面综述的研究。