Abstract: 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.

Key words: remote sensing imagery, intelligent interpretation, domain knowledge, deep learning, artificial intelligence, review