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pro vyhledávání: '"Hyperspectral image (HSI)"'
Akademický článek
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Autor:
Yuqi Liu, Enshuo Zhu, Qinghe Wang, Junhong Li, Shujun Liu, Yaowen Hu, Yuhang Han, Guoxiong Zhou, Renxiang Guan
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1139-1152 (2025)
Graph convolution subspace clustering has been widely used in the field of hyperspectral image (HSI) unsupervised classification due to its ability to aggregate neighborhood information. However, existing methods focus on using graph convolution tech
Externí odkaz:
https://doaj.org/article/6f45bcf7fd7f4826abe31e33b955c2b7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1329-1344 (2025)
Domain adaptation has been proven effective for addressing cross-domain hyperspectral image (HSI) classification, especially when the target domain has no labeled samples. Current domain adaptation algorithms focus on finding domain-invariant subspac
Externí odkaz:
https://doaj.org/article/4a211016184347d196986592a375af8b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1196-1211 (2025)
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) are widely used due to their ability to leverage the rich spectral information across multiple bands. However, HSI classification still faces various challenges, includ
Externí odkaz:
https://doaj.org/article/b644dd905935470cacc3ab699c3e9197
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 112-131 (2025)
Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been ef
Externí odkaz:
https://doaj.org/article/e87fcdd975e74ecbbcd27b68cfc077c1
Autor:
Cao, Cong-Yin1 (AUTHOR) 19913809590@stu.hunau.edu.cn, Li, Meng-Ting1 (AUTHOR) lmt@stu.hunau.edu.cn, Deng, Yang-Jun1 (AUTHOR) dyj2012@yeah.net, Ren, Longfei2 (AUTHOR) renlf@aircas.ac.cn, Liu, Yi1 (AUTHOR) zhuxh@hunau.net, Zhu, Xing-Hui1 (AUTHOR)
Publikováno v:
Remote Sensing. Nov2024, Vol. 16 Issue 22, p4287. 21p.
Autor:
Zhu, Xing-Hui1 (AUTHOR) zhuxh@hunau.net, Li, Kai-Run1 (AUTHOR) viperl1@stu.hunau.edu.cn, Deng, Yang-Jun1 (AUTHOR) dyj2012@yeah.net, Long, Chen-Feng1 (AUTHOR) tsq@hunau.edu.cn, Wang, Wei-Ye2 (AUTHOR) wwy@cuit.edu.cn, Tan, Si-Qiao1 (AUTHOR)
Publikováno v:
Remote Sensing. Nov2024, Vol. 16 Issue 21, p4055. 22p.
Autor:
Li, Sai1,2 (AUTHOR) lisai@uzz.edu.cn, Huang, Shuo3,4 (AUTHOR) huangshuo@mail.sitp.ac.cn
Publikováno v:
Remote Sensing. Nov2024, Vol. 16 Issue 21, p4050. 20p.
Autor:
Li, Rumei1 (AUTHOR) 2220902206@cnu.edu.cn, Zhang, Liyan1,2 (AUTHOR) 2230902185@cnu.edu.cn, Wang, Zun1 (AUTHOR) lixiaojuan@cnu.edu.cn, Li, Xiaojuan1,2 (AUTHOR)
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 21, p7023. 20p.
Publikováno v:
Shipin gongye ke-ji, Vol 45, Iss 17, Pp 345-351 (2024)
To evaluate the effectiveness of a deep learning which is based intelligent assisted hyperspectral imaging system on the detection of pork freshness indicators, volatile basic nitrogen (TVB-N), total viable count (TVC), and 900~2500 nm near-infrared
Externí odkaz:
https://doaj.org/article/66807ee9346842db8598d254de9fc021