A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images

A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images

Oil spills have devastating impacts on ocean ecosystems, leading to significant economic losses. Consequently, it is crucial to develop effective techniques for the rapid detection of marine oil spills using satellite observations. Existing methods for detecting oil spills using synthetic aperture r...

Full description

Saved in:
Bibliographic Details
Main Authors: Xudong Huang, Biao Zhang, William Perrie
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep learning (DL)
faster R-CNN
oil spill
synthetic aperture radar (SAR)
UNet<sup xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">++</sup>
Online Access:https://ieeexplore.ieee.org/document/10990149/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10990149/

Similar Items