Exploration of machine learning techniques for cloud removal and gap filling on sentinel-2 time series images for better exploitation in far North Cameroon

Abstract

The direct exploitation and interpretation of optical satellite images, such as Sentinel-2 data, is significantly hampered by cloud cover. In this paper, we explore several machine learning algorithms in order to suggest a machine learning-based method for cloud removal and gap filling in Sentinel-2 satellite pictures for better utilization in the far north of Cameroon, concentrating on the city of Maroua. Our goal is to successfully fill these gaps produced by these cloud masks in the photos by using data from several photographs taken on various dates, assuming that cloud occurrence changes both geographically and temporally. The cloud-covered and cloud-free regions are analyzed using a variety of machine learning methods, such as Random Forest, VGG16 with Random Forest, VGG with dense layers, VGG16 with image data augmentation, SVM, and deep learning CNN models. We assess the correctness of each model and compare their performance through rigorous experimentation. Our findings show that the VGG16 model with the addition of picture data had the best accuracy, at about 95%.