Cloud Segmentation in Multispectral Images from the Sentinel-2 Satellite: A Comparative Study of Deep Learning Approaches - Dias, Jean A. C.; Pereira, Williane G. S.; Negreiros, Waldemiro J. A. G.; Guimarães, Pedro H. do V.; Corrêa, Alan B. S.; Tamasauskas, Leonardo de O.; Gomes, Marivan S.; Souza, Danilo; Yoshida, William; Valente, Danilo; Rocha, Daniela; Augusto, Fernando; et al. (Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA); 2025: Anais do XVI Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, 2025)
To enhance the processing of multispectral images under adverse conditions, such as the presence of clouds, this study compared the neural networks U-Net, DeepLabV3+, and SegFormer using images from the Sentinel-2 satellite, evaluating their performance through metrics such as Intersection over Union (IoU) and Dice coefficient. The results indicate that SegFormer achieved the highest accuracy in cloud detection but with a longer inference time, while U-Net demonstrated a balance between accuracy and efficiency, and DeepLabV3+ stood out for its shorter processing time but lower performance. The study highlights the relevance of neural networks in remote sensing and indicates the need for model optimization.