Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Lianhui LIANG"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 1778-1791 (2025)
Extracting discriminative spectral-spatial features from hyperspectral images (HSIs) remains a crucial topic within the remote sensing community. However, most feature extraction methods suffer from coarse textures, leading to poor performance in cla
Externí odkaz:
https://doaj.org/article/37270ea018134777b71cf38dbf07841b
Evaluation of Kirby-Bauer disc diffusion test for antibiotic susceptibility of Neisseria gonorrhoeae
Publikováno v:
Pifu-xingbing zhenliaoxue zazhi, Vol 30, Iss 1, Pp 56-59 (2023)
Objective To evaluate the accuracy of disc diffusion method for detecting the susceptibility of Neisseria gonorrhoeae to seven surveillance drugs. Methods The agar dilution method and the disc diffusion method were compared in assessing the susceptib
Externí odkaz:
https://doaj.org/article/02cea65924644275a3035d2053a13ae8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 9336-9349 (2023)
The Tibetan Plateau is one of the broadest and highest collisional deformation belts in the world and its tectonic movement is crucial to the evolution of crustal structure and the formation of active geological disasters. Satellite Interferometric S
Externí odkaz:
https://doaj.org/article/74b5193d42514ccaab7e9f99ff4a9f6e
Publikováno v:
Remote Sensing, Vol 16, Iss 2, p 325 (2024)
Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their lim
Externí odkaz:
https://doaj.org/article/46cff689b10844d4ba866e12f6321236
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5401-5415 (2022)
Convolutional neural network (CNN) has been successfully introduced to hyperspectral image (HSI) classification and achieved effective performance. With the depth of the CNN increases, it may cause the gradient to become zero, and the structure lacks
Externí odkaz:
https://doaj.org/article/89b0be65ca9d4ed7a7b9a2d06710e907
Publikováno v:
Remote Sensing, Vol 15, Iss 7, p 1758 (2023)
Traditional convolutional neural networks (CNNs) can be applied to obtain the spectral-spatial feature information from hyperspectral images (HSIs). However, they often introduce significant redundant spatial feature information. The octave convoluti
Externí odkaz:
https://doaj.org/article/46f9ec819cfa4b3f9c07ea3076257835
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0bed04ba11f782f249aeff9d637fd9f8
https://doi.org/10.1109/igarss46834.2022.9884001
https://doi.org/10.1109/igarss46834.2022.9884001
Publikováno v:
2021 7th International Conference on Computer and Communications (ICCC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b507d1325d84d6e5bd57f184ef28f6f0
https://doi.org/10.1109/iccc54389.2021.9674345
https://doi.org/10.1109/iccc54389.2021.9674345
Publikováno v:
IOP Conference Series: Earth and Environmental Science. 783:012087
With the depth of the Convolutional neural network(CNN) increases, CNN may lead to the problem of gradient disappearance. Simultaneously, single scale convolutional kernel may not reflect the complex spatial structural information in hyperspectral im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41685551a901743aafdb3ccdf772076d
https://doi.org/10.1088/1755-1315/783/1/012087
https://doi.org/10.1088/1755-1315/783/1/012087
Autor:
Li, Shiping1 (AUTHOR) 22105010146@stu.wit.edu.cn, Liang, Lianhui2,3 (AUTHOR) lianglh0308@126.com, Zhang, Shaoquan4 (AUTHOR) zhangshaoquan1@163.com, Zhang, Ying2 (AUTHOR), Plaza, Antonio3 (AUTHOR), Wang, Xuehua1 (AUTHOR) 04012037@wit.edu.cn
Publikováno v:
Remote Sensing. Jan2024, Vol. 16 Issue 2, p325. 26p.