Automatic Recognition of Oil Spills Using Neural Networks and Classic Image Processing
Oil spill events are one of the major risks to marine and coastal ecosystems and, therefore, early detection is crucial for minimizing environmental contamination. Oil spill events have a unique appearance in satellite images created by Synthetic Aperture Radar (SAR) technology, because they are byp...
Main Authors: | Rotem Rousso, Neta Katz, Gull Sharon, Yehuda Glizerin, Eitan Kosman, Assaf Shuster |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-04-01
|
Series: | Water |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4441/14/7/1127 |
Similar Items
-
Oil Spill SAR Image Segmentation via Probability Distribution Modeling
by: Fang Chen, et al.
Published: (2022-01-01) -
Oil Spill Detection Using Machine Learning and Infrared Images
by: Thomas De Kerf, et al.
Published: (2020-12-01) -
Multifeature Semantic Complementation Network for Marine Oil Spill Localization and Segmentation Based on SAR Images
by: Jianchao Fan, et al.
Published: (2023-01-01) -
Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model
by: Peng Chen, et al.
Published: (2022-05-01) -
Multiscale Feature Fusion for Hyperspectral Marine Oil Spill Image Segmentation
by: Guorong Chen, et al.
Published: (2023-06-01)