在对地观测领域中云检测是遥感定量化应用的重要环节，同时也是卫星气象应用的关键步骤。近年来，基于机器学习的遥感影像云检测逐渐成为该领域的研究热点，并且取得了一系列研究成果。系统阐述了近10 a来国内外基于机器学习的遥感影像云检测的研究进展，将算法模型分为传统的机器学习模型和深度学习模型两类，并对两类中的具体模型进行详细介绍，对比分析不同模型的优缺点及其适用情况。重点介绍了传统机器学习中的支持向量机（SVM）、随机森林等方法，深度学习中的神经网络模型，包括卷积神经网络（CNN）、改进的U-Net网络等模型。在此基础上，分析了基于机器学习的遥感影像云检测研究中存在的问题，讨论了未来潜在发展方向。

In the field of earth observation， cloud detection is not only an important part in the quantitative application of remote sensing， but also a key step in the application of satellite meteorology. In recent years， remote sensing image cloud detection based on machine learning has gradually become a research hotspot in this field， and a series of research achievements have been obtained. Systematically describes the research progress of remote sensing image cloud detection based on machine learning at home and abroad in recent 10 years， dividing the algorithm models into traditional machine learning model and deep learning model. Moreover， the specific models of two categories are introduced in detail， and the advantages， disadvantages and applications of different models are compared and analyzed. This paper focuses on the Support Vector Machine （SVM）， random forest and other methods in traditional machine learning， and the neural network models in deep learning， including Convolutional Neural Network （CNN）， improved U-Net network and so on. On this basis， the existing problems in the research of remote sensing image cloud detection based on machine learning are analyzed， and the potential development direction in the future is discussed.