With the rapid development of remote sensing technology, the intelligent decoding of weak targets in optical remote sensing images has become one of the research hotspots in remote sensing information processing. The feature targets of remote sensing images are often characterized by small scale, many types, a large number, fast moving speed of some key small targets, and are easily affected by the complex background environment and noise, which makes it a great challenge extract information from weak targets in remote sensing images. Early research on weak target segmentation, detection, and tracking algorithms in intelligent interpretation algorithms mostly relied on template matching and a priori knowledge, and such algorithms need to consume a lot of resources, arithmetic, and expert knowledge costs, and there were problems of large computational volume and poor generalization ability. In recent years, with the rapid development of deep learning and other artificial intelligence technologies, the information of weak targets can be accurately obtained in massive remote sensing data, and the features of weak targets can be quickly extracted by combining deep learning algorithms to provide efficient and accurate decoding information. This paper summarizes the research progress of intelligent interpretation algorithms for weak targets in remote sensing images, including weak target segmentation, detection, and tracking algorithms based on traditional image processing methods, as well as typical related algorithms based on deep learning. By analyzing the advantages and limitations of these methods, it is of great significance to improve the information acquisition ability of relevant targets, enhance the situational awareness level of observation, and future applications.