Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Kuiliang Gao"'
Publikováno v:
BMC Public Health, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background Currently, obesity has been recognized to be an independent risk factor for osteoarthritis (OA), and the Metabolic Score for Visceral Fat (METS-VF) has been suggested to be potentially more accurate than body mass index (BMI) in t
Externí odkaz:
https://doaj.org/article/71a44b45cb5247e993a68c61ddc52507
Autor:
Kuiliang Gao, Chao Zhang, Yifan Zhang, Longyao Zhang, Jiankang Xu, Hongfei Xue, Lingling Jiang, Jinwei Zhang
Publikováno v:
BMC Nephrology, Vol 25, Iss 1, Pp 1-9 (2024)
Abstract Objective Chronic kidney disease (CKD) and osteoarthritis (OA) represent two frequently seen disorders among the general population, and they share several similar risk factors. The present work focused on assessing the relation of CKD with
Externí odkaz:
https://doaj.org/article/a6bf03161c2349b68127d8237cab495e
Publikováno v:
European Journal of Remote Sensing, Vol 55, Iss 1, Pp 103-114 (2022)
In recent years, the wide use of deep learning based methods has greatly improved the classification performance of hyperspectral image (HSI). As an effective method to improve the performance of deep convolution networks, attention mechanism is also
Externí odkaz:
https://doaj.org/article/4a73b3769f4946338d5aaebf0a160ae3
Publikováno v:
IET Image Processing, Vol 16, Iss 1, Pp 79-91 (2022)
Abstract To address the existing problems of capsule networks in deep feature extraction and spatial‐spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D c
Externí odkaz:
https://doaj.org/article/687af8a2b08a412c9b3f489a633e620c
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3398 (2023)
Recently, unsupervised domain adaptation (UDA) segmentation of remote sensing images (RSIs) has attracted a lot of attention. However, the performance of such methods still lags far behind that of their supervised counterparts. To this end, this pape
Externí odkaz:
https://doaj.org/article/ed1d1bd34009439cb68a6a0c36ca70c0
Publikováno v:
European Journal of Remote Sensing, Vol 54, Iss 1, Pp 385-397 (2021)
Deep learning based methods have recently been successfully explored in hyperspectral image classification field. However, training a deep learning model still requires a large number of labeled samples, which is usually impractical in hyperspectral
Externí odkaz:
https://doaj.org/article/f14213bd9c1f4a7ea06f135a90f763a8
Publikováno v:
IEEE Access, Vol 8, Pp 117096-117108 (2020)
Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this pa
Externí odkaz:
https://doaj.org/article/3fc64c73ca44459e819530789d9c8ce3
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 3462-3477 (2020)
Recently, the deep learning models have achieved great success in hyperspectral images (HSI) classification. However, most of the deep learning models fail to obtain satisfactory results under the condition of small samples due to the contradiction b
Externí odkaz:
https://doaj.org/article/4219e648d3dc44219e580b28eba5ff5b
Publikováno v:
Remote Sensing, Vol 15, Iss 7, p 1869 (2023)
The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods. In order to deal with the problem of small s
Externí odkaz:
https://doaj.org/article/624351425f9941ddb0f1db85e7b7ecf7
Publikováno v:
Remote Sensing, Vol 15, Iss 4, p 1080 (2023)
Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (H
Externí odkaz:
https://doaj.org/article/b3eb98e123c0476b8722bed9f96ba255