High-Resolution Digital Bathymetric Model (DBM) With Uncertainty Reconstructed by Deep Learning With Dropout-Reference-Cited by-同舟云学术

High-Resolution Digital Bathymetric Model (DBM) With Uncertainty Reconstructed by Deep Learning With Dropout

Published:2024 Issue: Volume:62 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Yang¹^ORCID,Li Sanzhong¹,Yan Weichao¹,Suo Yanhui¹,Wang Liangliang¹,Zou Zhuoyan¹,Wu Lixin²

Affiliation:

1. Frontiers Science Center for Deep Ocean Multispheres and Earth System, Key Laboratory of Submarine Geosciences and Prospecting Techniques, MOE and College of Marine Geosciences, Ocean University of China, Qingdao, China

2. Frontiers Science Center for Deep Ocean Multispheres and Earth System, Key Laboratory of Physical Oceanography, MOE, Ocean University of China, Qingdao, China

Funder

Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Higher Education Discipline Innovation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10518152.pdf?arnumber=10518152

Reference60 articles.

1. Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope

2. High-Resolution Maps of Bathymetry and Benthic Habitats in Shallow-Water Environments Using Multispectral Remote Sensing Imagery

3. Honing in on the climate signal in seafloor topography

4. Sensitivity of seafloor bathymetry to climate-driven fluctuations in mid-ocean ridge magma supply

5. Multibeam echo sounder data acquisition in Australia and beyond—User needs summary;Amirebrahimi;Geosci. Aust. Rec.,2019