A Deep Learning Model for Green Algae Detection on SAR Images

Author:

Guo Yuan1,Gao Le1ORCID,Li Xiaofeng1ORCID

Affiliation:

1. Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and the Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China

Funder

“14th Five-Year Plan” of Pilot National Laboratory for Marine Science and Technology

Joint Project of the National Natural Science Foundation of China-Shandong Province

National Natural Science Foundation of China

Strategic Priority Research Program of the Chinese Academy of Sciences

Scientific and Technological Innovation Projects in Shandong Province

CAS Program

Key Project of Center for Ocean Mega-Science, CAS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

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1. Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery;Earth System Science Data;2024-09-13

2. Super-Pixel Fisher Vector-based Green Algae Detection in Multispectral Remote Sensing Images;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images;International Journal of Remote Sensing;2024-06-25

4. A Deep-learning-based Frontal Detection in the China Sea;2024 Photonics & Electromagnetics Research Symposium (PIERS);2024-04-21

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