随着遥感技术的快速发展和应用需求的不断提升，遥感影像的智能解译已成为研究的热点。而知识作为对特定领域或现象的理解、经验和信息，能帮助解译模型更好地解释和处理遥感数据，除了能够提升解译精度、减少对标注数据的依赖之外，还能在复杂、不确定场景下增强模型的鲁棒性，为应对多源异构遥感数据的智能处理提供了关键支撑。本文首先介绍了知识引导下的遥感影像智能解译方法的发展历程，归纳了当下常用于解译任务中的知识类型，并从多种层次总结和比较了不同知识驱动方法的有效性和先进性；最后对知识引导下的遥感影像智能解译方法进行了总结与展望。

With the rapid advancement of remote sensing technology and the growing demand for its applications, intelligent interpretation of remote sensing imagery has emerged as a prominent research focus. Knowledge, defined as the comprehension, experience, and information about specific domains or phenomena, plays a crucial role in enhancing interpretation models' capacity to ana-lyze and process remote sensing data. Beyond improving interpretation accuracy and reducing dependence on annotated data, it significantly strengthens model robustness in complex and uncertain scenarios, thereby providing essential support for the intel-ligent processing of multi-source heterogeneous remote sensing data. This paper systematically examines the evolutionary trajec-tory of knowledge-guided intelligent interpretation methods for remote sensing imagery. It then categorizes and summarizes the commonly used types of knowledge in interpretation tasks, evaluating and comparing the effectiveness and advancement of dif-ferent knowledge-driven approaches at multiple levels. Finally, the paper provides a summary and outlook on the future devel-opment of knowledge-guided intelligent interpretation methods for remote sensing imagery.