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Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification
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“Inspired by the progress achieved in DL-based image super-resolution, many DL based remote sensing image fusion methods have been proposed in the last few years, indicating the distinct advantages over traditional methods owing to the better capability of DL models in characterizing the complex relationship between the input and the target images (Liu et al., 2018).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Liu et al., 2018
In the citing paper
“In other words, a systematic and comprehensive review concerning the application of DL in the field of remote sensing has not yet been conducted.”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Liu et al., 2018
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Cited by
Deep learning in remote sensing applications: A meta-analysis and review
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Metadata
- Authors
- Liu, T., & Abd-Elrahman, A.
- Affiliations
- University of Florida (US)
- Corresponding author
- Abd-Elrahman, Amr
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 139
- Pages
- 154-170
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2794055043
- Cited by
- 103
- References
- 78
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 13 · 2022: 11 · 2021: 20 · 2020: 23 · 2019: 12 · 2018: 8
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Funders
- U.S. Army Corps of Engineers
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
- Keywords
- deep convolutional neural networks, object-based image analysis, training data enrichment, land cover classification, wetland classification, orthophotos, voting classification, limited training data
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