Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters

Fahim Abdul Gafoor; Maryam R. Al Shehhi

doi:10.1109/IGARSS46834.2022.9883723

Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters

Fahim Abdul Gafoor
, Maryam R. Al Shehhi

Civil & Environmental Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Oil spills can cause long-term damage to the marine ecosystem and force desalination plants to shut down. Thus, this study aims to detect the oil spill in Arabian Gulf which produces a quarter of the world's oil. Sentinel-1 SAR and Sentinel-2 spectral imageries are utilized to detect oil spill. Sentinel-1 SAR is used to perform semantic segmentation with deep convolutional neural networks (DCNNs) using a U-Net structure and Sentinel-2 band ratio for mapping oil spill. Based on the segmentation results, the intersection-over-union (IoU) shows low values of <0.1 for the oil spill incidents along the Gulf coasts, but higher values for the offshore oil spill incidents (>0.65). Coastal land has adversely affected the segmentation process. As for the band ratio approach, it succeeded in distinguishing the oil spill, however prior knowledge or an expert interpretation is needed to verify the presence of the spill.

Original language	British English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6745-6748
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883723
State	Published - 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation
SDG 14 Life Below Water

Keywords

DCNN
Oil spill
SAR

Access to Document

10.1109/IGARSS46834.2022.9883723

OpenUrl availability

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Cite this

Gafoor, F. A., & Al Shehhi, M. R. (2022). Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 6745-6748). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883723

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title = "Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters",

abstract = "Oil spills can cause long-term damage to the marine ecosystem and force desalination plants to shut down. Thus, this study aims to detect the oil spill in Arabian Gulf which produces a quarter of the world's oil. Sentinel-1 SAR and Sentinel-2 spectral imageries are utilized to detect oil spill. Sentinel-1 SAR is used to perform semantic segmentation with deep convolutional neural networks (DCNNs) using a U-Net structure and Sentinel-2 band ratio for mapping oil spill. Based on the segmentation results, the intersection-over-union (IoU) shows low values of <0.1 for the oil spill incidents along the Gulf coasts, but higher values for the offshore oil spill incidents (>0.65). Coastal land has adversely affected the segmentation process. As for the band ratio approach, it succeeded in distinguishing the oil spill, however prior knowledge or an expert interpretation is needed to verify the presence of the spill.",

keywords = "DCNN, Oil spill, SAR",

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Gafoor, FA & Al Shehhi, MR 2022, Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 6745-6748, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883723

Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters. / Gafoor, Fahim Abdul; Al Shehhi, Maryam R.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6745-6748 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gafoor, Fahim Abdul

AU - Al Shehhi, Maryam R.

PY - 2022

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N2 - Oil spills can cause long-term damage to the marine ecosystem and force desalination plants to shut down. Thus, this study aims to detect the oil spill in Arabian Gulf which produces a quarter of the world's oil. Sentinel-1 SAR and Sentinel-2 spectral imageries are utilized to detect oil spill. Sentinel-1 SAR is used to perform semantic segmentation with deep convolutional neural networks (DCNNs) using a U-Net structure and Sentinel-2 band ratio for mapping oil spill. Based on the segmentation results, the intersection-over-union (IoU) shows low values of <0.1 for the oil spill incidents along the Gulf coasts, but higher values for the offshore oil spill incidents (>0.65). Coastal land has adversely affected the segmentation process. As for the band ratio approach, it succeeded in distinguishing the oil spill, however prior knowledge or an expert interpretation is needed to verify the presence of the spill.

AB - Oil spills can cause long-term damage to the marine ecosystem and force desalination plants to shut down. Thus, this study aims to detect the oil spill in Arabian Gulf which produces a quarter of the world's oil. Sentinel-1 SAR and Sentinel-2 spectral imageries are utilized to detect oil spill. Sentinel-1 SAR is used to perform semantic segmentation with deep convolutional neural networks (DCNNs) using a U-Net structure and Sentinel-2 band ratio for mapping oil spill. Based on the segmentation results, the intersection-over-union (IoU) shows low values of <0.1 for the oil spill incidents along the Gulf coasts, but higher values for the offshore oil spill incidents (>0.65). Coastal land has adversely affected the segmentation process. As for the band ratio approach, it succeeded in distinguishing the oil spill, however prior knowledge or an expert interpretation is needed to verify the presence of the spill.

KW - DCNN

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KW - SAR

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M3 - Conference contribution

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Gafoor FA, Al Shehhi MR. Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 6745-6748. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883723

Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

OpenUrl availability

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