Using ensemble methods to improve the robustness of deep learning for image classification in marine environments-Reference-Cited by-同舟云学术

Using ensemble methods to improve the robustness of deep learning for image classification in marine environments

Published:2022-03-18 Issue:6 Volume:13 Page:1317-1328
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Wyatt Mathew¹²³^ORCID,Radford Ben¹²³^ORCID,Callow Nikolaus²⁴^ORCID,Bennamoun Mohammed³⁵^ORCID,Hickey Sharyn²³⁴^ORCID

Affiliation:

1. The Australian Institute of Marine Science Indian Ocean Marine Research Centre Crawley WA Australia

2. UWA School of Agriculture and Environment The University of Western Australia Crawley WA Australia

3. UWA Oceans Institute The University of Western Australia Crawley WA Australia

4. Centre for Water and Spatial Science The University of Western Australia Crawley WA Australia

5. UWA Department of Computer Science and Software Engineering The University of Western Australia Crawley WA Australia

Funder

Department of Foreign Affairs and Trade, Australian Government

Australian Institute of Marine Science

Publisher

Wiley

Subject

Ecological Modeling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13841

Reference75 articles.

1. Akkaynak D. &Treibitz T.(2018).A revised underwater image formation model. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition 6723–6732.

2. Akkaynak D. &Treibitz T.(2019).Sea‐Thru: A method for removing water from underwater images. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1682–1691.

3. Alvarez‐Melis D. &Fusi N.(2020).Geometric dataset distances via optimal transport.arXiv [cs.LG]. arXiv.http://arxiv.org/abs/2002.02923

4. Australian Institute of Marine Science (AIMS). (2018).NWSSRP Theme 4—Understanding the Isolated Coral Atolls of the North West Shelf (Rowley Shoals). Retrieved fromhttps://apps.aims.gov.au/metadata/view/d7a7d2fe‐7920‐45e9‐8c81‐1aac24ebf73c

5. LeafNet: A computer vision system for automatic plant species identification

Cited by 11 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 remote sensing model for coral recruitment habitat;Remote Sensing of Environment;2024-09

2. Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond;Neural Computing and Applications;2024-08-02

3. Satellite remote sensing and bathymetry co-driven deep neural network for coral reef shallow water benthic habitat classification;International Journal of Applied Earth Observation and Geoinformation;2024-08

4. A survey on underwater coral image segmentation based on deep learning;Geo-spatial Information Science;2024-05

5. CNet: A Novel Seabed Coral Reef Image Segmentation Approach Based on Deep Learning;2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW);2024-01-01