Cloud Segmentation in Multispectral Images from the Sentinel-2 Satellite: A Comparative Study of Deep Learning Approaches
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Jean A. C. Dias, Williane G. S. Pereira, Waldemiro J. A. G. Negreiros, Pedro H. do V. Guimarães, Alan B. S. Corrêa, Leonardo de O. Tamasauskas, Marivan S. Gomes, Danilo Souza, William Yoshida, Danilo Valente, Daniela Rocha, Fernando Augusto, Gabriel B. Costa, Marcos C. da R. Seruffo
Institutions: Universidade Federal do Pará, University of East Anglia
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412606003