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.

With the continuous advancement of remote sensing technology and its growing importance, the need for ready-to-use data has increased exponentially. Satellite platforms such as Sentinel-2, which carries the Multispectral Instrument (MSI) sensor, known for their cost-effectiveness, capture valuable information about Earth in the form of images. However, they encounter a significant challenge in the form of clouds and their shadows, which hinders the data acquisition and processing for regions of interest. This article undertakes a comprehensive literature review to systematically analyze the critical cloud-related challenges. It explores the need for accurate cloud detection, reviews existing datasets, and evaluates contemporary cloud detection methodologies, including their strengths and limitations. Additionally, it highlights the inaccuracies introduced by varying atmospheric and environmental conditions, emphasizing the importance of integrating advanced techniques that can utilize local and global semantics. The review also introduces a structured intercomparison framework to enable standardized evaluation across binary and multiclass cloud detection methods using both qualitative and quantitative metrics. To facilitate fair comparison, a conversion mechanism is highlighted to harmonize outputs across methods with different class granularities. By identifying gaps in current practices and datasets, the study highlights the importance of innovative, efficient, and scalable solutions for automated cloud detection, paving the way for unbiased evaluation and improved utilization of satellite imagery across diverse applications.

This study explores the potential of improving vision transformer, MLP-Mixer, and global filter network for cloud detection using Sentinel-2 images. A mechanism is proposed to embed K-means clustering into frameworks for cloud detection (KET-CD) to enhance the performance. A comparative analysis of these architectures for multiclass cloud detection is conducted by leveraging two standard datasets: IndiaS2 and KappaSet. Our results demonstrate that the KET-CD methods achieve remarkable accuracy by outperforming the baseline architectures and state-of-the-art cloud detection methods (Fmask and Sen2Cor), exceeding a 0.5 mean intersection over union score. By incorporating cluster label embedding, KET-CD significantly improves the ability of these models to detect clouds and their shadows effectively in complex regions. These findings validate the importance of transformer-inspired architectures in processing large-scale satellite data for cloud detection tasks and highlight KET-CD as a promising solution for accurate cloud detection in remote sensing applications.