随着航空航天、遥感和通信等技术的快速发展，5G等高效通信技术的革新，遥感边缘智能（edge intelligence）成为当下备受关注的研究课题。遥感边缘智能技术通过将遥感数据处理与分析技术前置实现，在近数据源的位置进行高效地遥感信息分析和决策，在卫星在轨处理解译、无人机动态实时跟踪、大规模城市环境重建和无人驾驶识别规划等应用场景中起着至关重要的作用。本文对边缘智能在遥感数据解译中的研究现状进行了归纳总结，介绍了目前遥感智能算法模型在边缘设备进行部署应用中面临的主要问题，即数据样本的限制、计算资源的限制以及灾难性遗忘问题等。针对问题具体阐述了解决思路和主要技术途径，包括小样本情况下的泛化学习方法，详细介绍了样本生成和知识复用两种解决思路；轻量化模型的设计与训练，分析了模型剪枝和量化等方法以及基于知识蒸馏的训练框架；面向多任务的持续学习方法，对比了样本数据重现和模型结构扩展两种原理。同时，还结合了典型的遥感边缘智能应用，对代表性算法的优势和不足进行了深层剖析。最后介绍了遥感边缘智能面临的挑战，以及未来技术的主要发展方向。

Remote sensing edge intelligence technology has become an important research topic due to the rapid development of aerospace, remote sensing, and communication as well as the innovation of 5G and other efficient communication technologies. Remote sensing edge intelligence technology aims to achieve the front of intelligent application and perform efficient information analysis and decision making at a location close to the data source. This technology can be effectively used in satellite on-orbit processing and interpretation, unmanned aerial vehicle(UAV) dynamic real-time tracking, large-scale urban environment reconstruction, automatic driving recognition planning, and other scenarios to saving a considerable amount of transmission bandwidth, processing time, and resource consumption and achieve fast, accurate, and compact design of intelligent technology algorithm. We summarize the research status of edge intelligence in remote sensing in this study. First, we discuss the problems faced by the current remote sensing field in deployment of applications on edge devices, namely, 1) limitation of number of samples: compared with visual scene images, remote sensing data continue to be a problem of small samples. Remote sensing scenes contain a large number of complex backgrounds and target categories, but the actual number of effective samples is relatively small. Newly emerged and modified targets typically face serious problems of uneven distribution of categories. 2) Limitation of computing resources: coverage area of remote sensing images that can generally reach several or even hundreds of kilometers and data size of a single image that can reach up to several hundred GBs require a large amount of storage space for edge devices. In addition, the increasing complexity of deep learning models increases the requirements for computing power resources. Therefore, remote sensing edge intelligence must solve the contradiction between model complexity and power consumption on edge devices. 3) Catastrophic forgetting: new tasks and categories continue to emerge in the analysis of remote sensing data. Existing algorithms have poor generalization ability for continuous input data. Hence, continuous learning must also be solved to maintain high accuracy and high performance of algorithms. We then introduce solutions and primary technical approaches to related problems, including generalized learning in the case of small samples, design and training strategy of the lightweight model, and continuous learning for multitasks. 1) Generalized learning in the case of small samples: we summarize existing solutions into two categories, namely, combine characteristics of remote sensing images to expand the sample intelligently and meet data requirements of the model training as well as introduce priority knowledge from the perspective of knowledge reuse through different learning strategies, such as transfer learning, meta- learning, and metric learning, to assist the model in learning new categories and reduce the model's need for remote sensing data. 2) Design and training strategy of the lightweight model: the former introduces convolution calculation unit design, artificial network design, automatic design, model pruning and quantification methods, while the latter compares training frameworks based on knowledge distillation and traditional training methods. 3) Continuous learning for multitasks: the first category is based on the reproduction of sample data. The model plays back stored samples while learning new tasks by storing samples of previous tasks or applying a generated model to generate pseudo samples to balance the training data of different tasks and reduce the problem of catastrophic forgetting. The second category is based on the method of model structure expansion. The model is divided into subsets dedicated to each task by constraining parameter update strategies or isolating model parameters. The method of model structure expansion improves the task adaptability of the model and avoids catastrophic forgetting without relying on historical data. Furthermore, combined with typical applications of remote sensing edge intelligence technology, we analyze the advantages and disadvantages of representative algorithms. Finally, we discuss challenges faced by remote sensing edge intelligence technology and future directions of this study. Further research is required in remote sensing edge intelligence technology to improve its depth and breadth of application.