Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Laiying Fu"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea l
Externí odkaz:
https://doaj.org/article/4c006f8e9c154c388157f4959fc08990
Publikováno v:
Applied Sciences, Vol 14, Iss 20, p 9499 (2024)
In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classif
Externí odkaz:
https://doaj.org/article/425b78f2e69340589f84e8410c0070a9
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4471 (2023)
With the continuous advancement of deep learning technology, researchers have made further progress in the hyperspectral image (HSI) classification domain. We propose a double-branch multi-scale residual network (DBMSRN) framework for HSI classificat
Externí odkaz:
https://doaj.org/article/a5749839005b47148737f5b7dd00afd3
Autor:
Li, Xiao1,2 (AUTHOR), Zhou, Shijian3 (AUTHOR) shjzhou@nchu.edu.cn, Wang, Fengwei4 (AUTHOR), Fu, Laiying5 (AUTHOR)
Publikováno v:
Scientific Reports. 2/24/2024, Vol. 14 Issue 1, p1-15. 15p.
Publikováno v:
Applied Sciences (2076-3417); Oct2024, Vol. 14 Issue 20, p9499, 24p
Autor:
Fu, Laiying1,2 (AUTHOR) fulaiying@ecut.edu.cn, Chen, Xiaoyong2,3 (AUTHOR), Pirasteh, Saied4,5 (AUTHOR) sapirasteh1@usx.edu.cn, Xu, Yanan2 (AUTHOR)
Publikováno v:
Remote Sensing. Sep2023, Vol. 15 Issue 18, p4471. 21p.