Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004–2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.

云探测是卫星遥感中必不可少的重要过程。研究人员提出了多种云检测方法。本文回顾了有关云检测的最新文献（2004–2018）。文献报道了使用遥感卫星图像检测云的各种技术。研究人员使用各种机器学习方法和经典算法探索了各种形式的云检测，例如云/无云，雪/云和瘦云/厚云。机器学习方法从训练数据中学习，经典算法方法是使用不同图像参数的阈值实现的。基于阈值的方法的通用性很差，因为值随位置而变化。许多模型都不包括基于地面的估计值的验证。使用机器学习的混合方法，