Cited by 571Remote-Sensing Image ClassificationAdvanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

The DOI matches its Crossref record; 137 of the 138 references it cites were found in a registry or an index.

PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval

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Zhou, Weixun, Newsam, Shawn, Li, Congmin, Shao, Zhenfeng·2018·ISPRS Journal of Photogrammetry and Remote Sensing

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Crossref · 10.1016/j.isprsjprs.2018.01.004

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Metadata

Authors
Zhou, W., Newsam, S., Li, C., & Shao, Z.
Affiliations
  • Wuhan University (CN)
  • University of California, Merced (US)
Corresponding author
Shao, Zhenfeng
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2018-11
Volume
145
Pages
197-209
ISSN
0924-2716 (ISSN-L)
OpenAlex
W2626107033
Cited by
571
References
138
Citations by year
2026: 64 · 2025: 79 · 2024: 78 · 2023: 86 · 2022: 74 · 2021: 65 · 2020: 61 · 2019: 44 · 2018: 19
FWCI
35.81
Type
journal article
Language
en
Open access
Locations
  • ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Key Technologies Research and Development Program (2016YFB0502603)
  • Fundamental Research Funds for the Central Universities (2042016kf0179, 2042016kf1019, 2016070204010114)
  • Wuhan Chen Guang Project
  • Guangzhou science and technology project (201604020070)
  • Special task of technical innovation in Hubei Province (2016AAA018)
  • National Natural Science Foundation of China (61671332, 41771452, 41771454)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
remote sensing image retrieval, benchmark dataset, high-resolution imagery, land use and land cover classification, deep learning, performance evaluation, background noise

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