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 analyze and process remote sensing data. In addition to improving interpretation accuracy and reducing dependence on annotated data, knowledge significantly strengthens model robustness in complex and uncertain scenarios, thereby providing essential support for the intelligent processing of multi-source heterogeneous remote sensing data. This paper first reviews the evolutionary trajectory of knowledge-guided intelligent interpretation methods for remote sensing imagery, subsequently summarizes the commonly used types of knowledge in interpretation tasks, and then proceeds to compare the effectiveness and advancement of different knowledge-driven approaches at multiple levels. Finally, the paper provides a summary and outlook on the future development of knowledge-guided intelligent interpretation methods for remote sensing imagery.