High-resolution benthic habitat mapping from machine learning on PlanetScope imagery and ICESat-2 data-Reference-Cited by-同舟云学术

High-resolution benthic habitat mapping from machine learning on PlanetScope imagery and ICESat-2 data

Published:2023-03-07 Issue:1 Volume:38 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Van An Nguyen¹^ORCID,Quang Nguyen Hao²³^ORCID,Son Tong Phuoc Hoang⁴,An Tran Thi⁵^ORCID

Affiliation:

1. Faculty of Geography, University of Science and Education, The University of Danang, Da Nang City, Vietnam

2. Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam

3. Faculty of Environment, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

4. Institute of Oceanography – Vietnam Academy of Science and Technology, Khanh Hoa Province, Vietnam

5. Faculty of Management Science, Thu Dau Mot University, Binh Duong Province, Vietnam

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2023.2184875

Reference78 articles.

1. Abadi M Barham P Chen J Chen Z Davis A Dean J Devin M Ghemawat S Irving G Isard M. 2016. Tensorflow: a system for large-scale machine learning Proceeding of the Symposium Osdi 16 November 2–4 2016 Savannah GA USA 265–283.

2. Andréfouët S Hochberg E Chevillon C Muller-Karger F Brock J Hu C. 2007. Multi-scale remote sensing of coral reefs. In: Miller RL Del Castillo CE Mckee BA editors. Remote sensing of coastal aquatic environments. Remote sensing and digital image processing Vol. 7. Dordrecht: Springer pp. 297–315.

3. Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

4. Scale mixtures of normal distributions;Andrews DF;J Roy Stat Soc. Series B (Methodological),1974

5. Materials on fauna of anemonefishes (Pomacentridae, Amphiprioninae) and their host sea anemones (cnidaria, actiniaria) on reefs of Ly Son Islands (South China Sea, Central Vietnam))

Cited by 4 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. Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon;Geosciences;2024-05-11

2. Analysis of ICESat‐2 Data Acquisition Algorithm Parameter Enhancements to Improve Worldwide Bathymetric Coverage;Earth and Space Science;2024-02

3. Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models;GIScience & Remote Sensing;2024-01-16

4. Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images;Sensors;2023-10-27