Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of intensive machine intelligence to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of artificial intelligence in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study presents a succinct, but comprehensive review of the application of machine intelligence techniques on radio images with emphasis on the morphological classification of radio galaxies. It aims to present a detailed synthesis of the relevant papers summarizing the literature based on data complexity, data pre-processing, and methodological novelty in radio astronomy. The rapid advancement and application of computer intelligence in radio astronomy has resulted in a revolution and a new paradigm shift in the automation of daunting data processes. However, the optimal exploitation of artificial intelligence in radio astronomy, calls for continued collaborative efforts in the creation of annotated data sets. Additionally, in order to quickly locate radio galaxies with similar or dissimilar physical characteristics, it is necessary to index the identified radio sources. Nonetheless, this issue has not been adequately addressed in the literature, making it an open area for further study.

翻译：现代射电望远镜（如平方公里阵列（SKA））每日将生成EB级规模的数据集。海量数据中蕴含着未知且罕见的天体物理现象，这些现象将引领科学发现。然而，这只有在充分运用机器智能以辅助人工和传统统计技术的前提下才具有可行性。近年来，聚焦于人工智能在射电天文学中应用的学术论文激增，研究重点涵盖源提取、形态分类及异常检测等挑战。本研究对机器学习技术在射电图像中的应用进行了简洁而全面的综述，特别关注射电星系的形态分类问题。本文旨在系统整合相关文献，基于数据复杂性、数据预处理及射电天文学方法创新性三个维度进行文献总结。计算机智能在射电天文学中的快速进步与应用，已引发数据自动化处理领域的革命性范式转变。然而，要实现人工智能在射电天文学中的最优应用，仍需持续开展标注数据集的协同建设工作。此外，为快速定位具有相似或相异物理特征的射电星系，有必要对已识别的射电源建立索引体系。然而该问题在现有文献中尚未得到充分论述，因此成为亟待深入研究的开放领域。