Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Baoxiang Huang"'
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
In coastal regions, the formation of submesoscale eddies is frequently influenced by factors including topography, tidal forces, and ocean currents. These eddies are often challenging to detect owing to the limited spatial resolution of altimeters an
Externí odkaz:
https://doaj.org/article/d039a4024a014eb68a48ff1632e911ca
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTThe capability of continuous eddy tracking has been significantly advanced over the past decades, thanks to the availability of sea surface height measurements routinely collected by satellite altimeters. However, due to the complexity of edd
Externí odkaz:
https://doaj.org/article/3e36d2d5abdc428bb656884445d0bb36
Autor:
Haichen Zhao, Xiaoya Zhang, Yuanxiang Gao, Lili Wang, Longyang Xiao, Shunli Liu, Baoxiang Huang, Zhiming Li
Publikováno v:
Heliyon, Vol 10, Iss 15, Pp e35115- (2024)
Problem: Previous studies had confirmed that some deep learning models had high diagnostic performance in staging liver fibrosis. However, training efficiency of models predicting liver fibrosis need to be improved to achieve rapid diagnosis and prec
Externí odkaz:
https://doaj.org/article/39a75bd573ce46b39105f7e7bb92e33a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18214-18222 (2024)
Deep chlorophyll maximum (DCM) is a common oceanographic phenomenon characterized by a significant peak in chlorophyll concentration at a specific depth below the ocean surface. DCM formation is closely related to factors, such as light availability,
Externí odkaz:
https://doaj.org/article/3b5191ba73af4300a33c7a40c088ec88
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11997-12007 (2024)
Ocean eddies have a significant impact on marine ecosystems and the climate because they transport essential substances in the ocean. Detection of ocean eddies has become one of the most active topics in physical ocean research. In recent years, rese
Externí odkaz:
https://doaj.org/article/6a9a4a47d1e14f82be93ed42c2f2b66a
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3278 (2024)
Arctic sea ice concentration plays a key role in the global ecosystem. However, accurate prediction of Arctic sea ice concentration remains a challenging task due to its inherent nonlinearity and complex spatiotemporal correlations. To address these
Externí odkaz:
https://doaj.org/article/299eaa279c644087970737e9a35260a4
Publikováno v:
Remote Sensing, Vol 16, Iss 7, p 1205 (2024)
Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST
Externí odkaz:
https://doaj.org/article/d60ea8fee2964512b55df300712ff884
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 9944-9950 (2023)
A subsurface chlorophyll maximum is an important ecological feature of planktonic ecosystems. Although the vertical profiles can be determined through the implementation of biogeochemical (BGC)-Argo buoy, this method is not compatible with the ocean
Externí odkaz:
https://doaj.org/article/ec78e1f052f34101a3dfa6d01a77a430
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7303-7314 (2023)
Wild fish recognition is a fundamental problem of ocean ecology research and contributes to the understanding of biodiversity. Given the huge number of wild fish species and unrecognized category, the essence of the problem is an open set fine-graine
Externí odkaz:
https://doaj.org/article/54d70ac087b74e548f13cf844d3c3863
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4413 (2023)
Remote sensing techniques for shoreline extraction are crucial for monitoring changes in erosion rates, surface hydrology, and ecosystem structure. In recent years, Convolutional neural networks (CNNs) have developed as a cutting-edge deep learning t
Externí odkaz:
https://doaj.org/article/d1eaf1bb46824fa18fb65b2f69ab39c8