Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms-Reference-Cited by-同舟云学术

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Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

Published:2021 Issue: Volume:14 Page:9071-9078
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Li Ying,Yu Qinglai,Xie Ming^ORCID,Zhang Zhenduo,Ma Zhanjun,Cao Kai

Funder

China National Key R&D Program

Liaoning Revitalization Talents Program

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9314330/09529039.pdf?arnumber=9529039

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Semi-Supervised Model for Fine-Grained Identification of Oil Emulsions on the Sea Surface Using Hyperspectral Imaging;Journal of the Indian Society of Remote Sensing;2024-07-02

2. The application of laser‑induced fluorescence in oil spill detection;Environmental Science and Pollution Research;2024-03-11

3. Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features;Acta Oceanologica Sinica;2024-03

4. Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network;Journal of Oceanology and Limnology;2024-02-28

5. Oil Spill Classification using Machine Learning;2024 11th International Conference on Computing for Sustainable Global Development (INDIACom);2024-02-28

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