Automated object recognition in high-resolution optical remote sensing imagery

This paper reports the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, as well as summarize the challenges, champion solutions, and future directions.

Detalles Bibliográficos
Autores principales: Yao, Yazhou, Chen, Tao, Bi, Hanbo, Cai, Xinhao, Pei, Gensheng, Yang, Guoye, Yan, Zhiyuan, Sun, Xian, Xu, Xing, Zhang, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
PERSPECTIVE
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265956/
https://www.ncbi.nlm.nih.gov/pubmed/37324647
http://dx.doi.org/10.1093/nsr/nwad122
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author Yao, Yazhou
Chen, Tao
Bi, Hanbo
Cai, Xinhao
Pei, Gensheng
Yang, Guoye
Yan, Zhiyuan
Sun, Xian
Xu, Xing
Zhang, Hai
author_facet Yao, Yazhou
Chen, Tao
Bi, Hanbo
Cai, Xinhao
Pei, Gensheng
Yang, Guoye
Yan, Zhiyuan
Sun, Xian
Xu, Xing
Zhang, Hai
author_sort Yao, Yazhou
collection PubMed
description This paper reports the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, as well as summarize the challenges, champion solutions, and future directions.
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spelling pubmed-102659562023-06-15 Automated object recognition in high-resolution optical remote sensing imagery Yao, Yazhou Chen, Tao Bi, Hanbo Cai, Xinhao Pei, Gensheng Yang, Guoye Yan, Zhiyuan Sun, Xian Xu, Xing Zhang, Hai Natl Sci Rev PERSPECTIVE This paper reports the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, as well as summarize the challenges, champion solutions, and future directions. Oxford University Press 2023-05-04 /pmc/articles/PMC10265956/ /pubmed/37324647 http://dx.doi.org/10.1093/nsr/nwad122 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle PERSPECTIVE
Yao, Yazhou
Chen, Tao
Bi, Hanbo
Cai, Xinhao
Pei, Gensheng
Yang, Guoye
Yan, Zhiyuan
Sun, Xian
Xu, Xing
Zhang, Hai
Automated object recognition in high-resolution optical remote sensing imagery
title Automated object recognition in high-resolution optical remote sensing imagery
title_full Automated object recognition in high-resolution optical remote sensing imagery
title_fullStr Automated object recognition in high-resolution optical remote sensing imagery
title_full_unstemmed Automated object recognition in high-resolution optical remote sensing imagery
title_short Automated object recognition in high-resolution optical remote sensing imagery
title_sort automated object recognition in high-resolution optical remote sensing imagery
topic PERSPECTIVE
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265956/
https://www.ncbi.nlm.nih.gov/pubmed/37324647
http://dx.doi.org/10.1093/nsr/nwad122
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