Model-Based Oil Slick Thickness Estimation Using Artificial Neural Network
Author:
Affiliation:
1. Chinese Academy of Sciences,State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute,Beijing,China,100101
2. Università Degli Studi di Napoli Parthenope,Dipartimento di Ingegneria,Napoli,Italy,80143
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282557.pdf?arnumber=10282557
Reference11 articles.
1. Radar Backscattering Over Sea Surface Oil Emulsions: Simulation and Observation;meng;IEEE Trans Geosci Remote Sens,2021
2. Oil-Spill-Response-Oriented Information Products Derived From a Rapid-Repeat Time Series of SAR Images
3. Wave damping by a thin layer of viscous fluid
4. Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR-C/X-SAR
5. Oil spill detection by imaging radars: Challenges and pitfalls
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1. Model-Based Neural Network to Retrieve Ancillary Information About Sea Oil Slicks;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
2. Scattering Model-Based Oil-Slick-Related Parameters Estimation From Radar Remote Sensing: Feasibility and Simulation Results;IEEE Transactions on Geoscience and Remote Sensing;2024
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