Evaluating Multiple Combinations of Models and Encoders to Segment Clouds in Satellite Images

Jocsan Ferreira, Leandro Silva, Mauricio Escarpinati, André Backes, João Mari

2024

Abstract

This work evaluates methods based on deep learning to perform cloud segmentation in satellite images. Wwe compared several semantic segmentation architectures using different encoder structures. In this sense, we fine-tuned three architectures (U-Net, LinkNet, and PSPNet) with four pre-trained encoders (ResNet-50, VGG-16, MobileNet V2, and EfficientNet B2). The performance of the models was evaluated using the Cloud-38 dataset. The training process was carried out until the validation loss stabilized, according to the early stopping criterion, which provides a comparative analysis of the best models and training strategies to perform cloud segmentation in satellite images. We evaluated the performance using classic evaluation metrics, i.e., pixel accuracy, mean pixel accuracy, mean IoU, and frequency-based IoU. Results demonstrated that the tested models are capable of segmenting clouds with considerable performance, with emphasis on the following values: (i) 96.19% pixel accuracy for LinkNet with VGG-16 encoder, (ii) 92.58% mean pixel accuracy for U-Net with MobileNet V2 encoder, (iii) 87.21% mean IoU for U-Net with VGG-16 encoder, and (iv) 92.89% frequency-based IoU for LinkNet with VGG-16 encoder. In short, the results of this study provide valuable information for developing satellite image analysis solutions in the context of precision agriculture.

Paper Citation

in Harvard Style

Ferreira J., Silva L., Escarpinati M., Backes A. and Mari J. (2024). Evaluating Multiple Combinations of Models and Encoders to Segment Clouds in Satellite Images. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 233-241. DOI: 10.5220/0012506700003660

in Bibtex Style

@conference{visapp24,
author={Jocsan Ferreira and Leandro Silva and Mauricio Escarpinati and André Backes and João Mari},
title={Evaluating Multiple Combinations of Models and Encoders to Segment Clouds in Satellite Images},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={233-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012506700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Evaluating Multiple Combinations of Models and Encoders to Segment Clouds in Satellite Images
SN - 978-989-758-679-8
AU - Ferreira J.
AU - Silva L.
AU - Escarpinati M.
AU - Backes A.
AU - Mari J.
PY - 2024
SP - 233
EP - 241
DO - 10.5220/0012506700003660
PB - SciTePress