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Authors: Abhishek Ramanathapura Satyanarayana and Maruf A. Dhali

Affiliation: Department of Artificial Intelligence, Bernoulli Institute, University of Groningen, 9747 AG Groningen, The Netherlands

Keyword(s): Oil Spill, Semantic Segmentation, Neural Networks, Deep Learning.

Abstract: Crude oil is an integral component of the world economy and transportation sectors. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unfortunate yet unavoidable. Even though oil spills are difficult to clean up, the first and foremost challenge is to detect them. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills remotely. The work examines and compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data to pave the way for further in-depth research. Multiple combinations of models are used to run the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and an improved class IoU of 61.549% for the “oil spill” class when compared with the previous benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the “oil spill” class (More)

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Paper citation in several formats:
Satyanarayana, A. R. and Dhali, M. A. (2025). Oil Spill Segmentation Using Deep Encoder-Decoder Models. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-730-6; ISSN 2184-4313, SciTePress, pages 741-748. DOI: 10.5220/0013259600003905

@conference{icpram25,
author={Abhishek Ramanathapura Satyanarayana and Maruf A. Dhali},
title={Oil Spill Segmentation Using Deep Encoder-Decoder Models},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2025},
pages={741-748},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013259600003905},
isbn={978-989-758-730-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Oil Spill Segmentation Using Deep Encoder-Decoder Models
SN - 978-989-758-730-6
IS - 2184-4313
AU - Satyanarayana, A.
AU - Dhali, M.
PY - 2025
SP - 741
EP - 748
DO - 10.5220/0013259600003905
PB - SciTePress

- Science and Technology Publications, Lda.
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