A High-Accuracy Nighttime Cloud Detection Algorithm Based on Contextual Features and Machine Learning

This paper proposes a generic algorithm to detect clouds in nighttime satellite images by using daytime image samples. The method aims to overcome the challenges of poor quality and inadequate number of nighttime image samples when detecting clouds based on machine learning (ML) during the night. Importantly, to precisely detect thin broken clouds and cloud edges during nighttime, we first use contextual features as input of the ML model to acquire more comprehensive information and enhance its ability to recognize these types of clouds. Nighttime images observed by the Gaofen-5 VIMI were utilized as an example to validate the performance of our algorithm. The result demonstrates that the addition of contextual features causes ML model to exhibit better performance and show a more accurate and refined detection of thin broken clouds and cloud edges, along with an overall accuracy of 91.38%, and a 3.64% improvement compared to the model without these features.