Automated Detection of Hydrothermal Emission Signatures From Multibeam Echo Sounder Images Using Deep Learning-Reference-Cited by-同舟云学术

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Automated Detection of Hydrothermal Emission Signatures From Multibeam Echo Sounder Images Using Deep Learning

Published:2023 Issue: Volume:16 Page:2703-2710
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:

Mimura Kazuhide¹^ORCID,Nakamura Kentaro²^ORCID,Takao Kazuhiro³,Yasukawa Kazutaka²,Kato Yasuhiro³

Affiliation:

1. Ocean Resources Research Center for Next Generation, Chiba Institute of Technology, Chiba, Japan

2. Frontier Research Center for Energy and Resources, School of Engineering, The University of Tokyo, Tokyo, Japan

3. Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan

Funder

Council for Science, Technology and Innovation

Cross-ministerial Strategic Innovation Promotion Program

Japan Society for the Promotion of Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10052641.pdf?arnumber=10052641

Reference41 articles.

1. Ultramafics-Hydrothermalism-Hydrogenesis-HyperSLiME (UltraH3) linkage: a key insight into early microbial ecosystem in the Archean deep-sea hydrothermal systems

3. Mid-Ocean Ridge Hydrothermal Fluxes and the Chemical Composition of the Ocean

4. Oceanic phosphorus imbalance: Magnitude of the mid-ocean ridge flank hydrothermal sink

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