Utilizing deep learning algorithms for automated oil spill detection in medium resolution optical imagery.
Mar Pollut Bull
; 206: 116777, 2024 Sep.
Article
in En
| MEDLINE
| ID: mdl-39083910
ABSTRACT
This study evaluates the performance of three typical convolutional neural network based deep learning algorithms for oil spill detection using medium-resolution optical satellite imagery from Sentinel-2 MSI, Landsat-8 OLI, and Landsat-9 OLI2. Oil slick training and validation dataset were created through a semi-automatic labeling approach, based on chronic and accidental oil spill cases reported worldwide. The research enhances UNet, BiSeNetV2, and DeepLabV3+ architectures by integrating attention mechanisms including the Squeeze-and-Excitation module (SE), Convolutional Block Attention Module (CBAM), and a Simple, parameter-free Attention Module (SimAM), analyzing the optimal model for oil spill detection. Notably, UNet integrated with CBAM, especially with sun glint as a feature, significantly outperformed others, achieving a micro-average F1 score of 88.8 %. This research highlights deep learning's potential in optical remote sensing for oil spill detection, stressing its escalating relevance with the growing deployment of medium- to high-resolution optical satellites.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Petroleum Pollution
/
Environmental Monitoring
/
Satellite Imagery
/
Deep Learning
Language:
En
Journal:
Mar Pollut Bull
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
United kingdom