Cloud detection using sentinel 2 imageries: a comparison of XGBoost, RF, SVM, and CNN algorithms

Abstract

This study reports results of the Extreme Gradient Boosting (XGBoost) algorithm in comparison to Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) to generate cloud mask comprising of six classes: low cloud, high cloud, cloud shadow, ground, snow, and water. Here, Baetens-Hagolle dataset and WHUS2-CD dataset was considered. Various texture features derived using GLCM, morphological profile analysis, bilateral filtering, and deep features derived using Residual Network (Resnet) were used in combination with spectral information. The first phase of the study suggested both XGBoost and RF exhibit comparable performance for both traditional texture features and deep features, the second phase highlighted that XGBoost showed better generalization capabilities with respect to the different environmental conditions, and finally, comparison with threshold-based methods (Function of Mask [Fmask], Sen2cor, and Maccs-Atcor Joint Method [MAJA]) suggested that XGBoost and RF based cloud detection is a good alternative of existing state-of-the-art cloud detection.