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Cited by 103Remote-Sensing Image ClassificationAdvanced Neural Network ApplicationsRemote Sensing and Land Use

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

Liu, Tao, Abd-Elrahman, Amr·2018·ISPRS Journal of Photogrammetry and Remote Sensing

Overview

Integrity signals

Registry

Crossref · 10.1016/j.isprsjprs.2018.03.006

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

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Claims other papers make about this one

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Citations

Cites

Research works printed but not linked: 2

  1. Gao, 2008, A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions, Online J. Earth Sci., 2, 27
  2. Torrey, 2009, Transfer learning, Handbook Res. Mach. Learn. Appl. Trends: Algorithms, Methods, Techniques, 1, 242

Cited by

See all 103

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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.