Oil Spill Segmentation Using Deep Encoder-Decoder Models

Abhishek Ramanathapura Satyanarayana, Maruf A. Dhali

2025

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

Paper Citation

in Harvard Style

Satyanarayana A. and Dhali M. (2025). Oil Spill Segmentation Using Deep Encoder-Decoder Models. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 741-748. DOI: 10.5220/0013259600003905

in Bibtex Style

@conference{icpram25,
author={Abhishek Satyanarayana and Maruf Dhali},
title={Oil Spill Segmentation Using Deep Encoder-Decoder Models},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={741-748},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013259600003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Oil Spill Segmentation Using Deep Encoder-Decoder Models
SN - 978-989-758-730-6
AU - Satyanarayana A.
AU - Dhali M.
PY - 2025
SP - 741
EP - 748
DO - 10.5220/0013259600003905
PB - SciTePress