Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Chunju, Zhang"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Typhoon disasters undergo a complex evolutionary process influenced by temporal changes, and investigating this process constitutes the central focus of geographical research. As a key node within the typhoon disaster process, the state serv
Externí odkaz:
https://doaj.org/article/c80f65f3b895485c95c9af827fe9807d
Autor:
Xiaoshuang Han, Shanshan Zhao, Ziheng Liu, Yujiang Zhang, Guoyu Zhao, Chunju Zhang, Lijuan Tang, Lin Cui, Yuanzhi Wang
Publikováno v:
International Journal for Parasitology: Parasites and Wildlife, Vol 24, Iss , Pp 100958- (2024)
Fleas are known to be vectors for a variety of pathogens in veterinary medicine. However, no information is available on the presence of Bartonella and Trypanosomatidae in fleas of the long-tailed ground squirrel (LTGR, Spermophilus undulatus). The p
Externí odkaz:
https://doaj.org/article/fb7a3c39a80f44a1b8d09ee142e18027
Publikováno v:
Remote Sensing, Vol 15, Iss 23, p 5617 (2023)
With the development of urban built-up areas, accurately extracting the urban built-up area and spatiotemporal pattern evolution trends could be valuable for understanding urban sprawl and human activities. Considering the coarse spatial resolution o
Externí odkaz:
https://doaj.org/article/7b3b1ff8d5f343459ef4aa158c485ed1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 10073-10086 (2022)
To achieve land cover change detection (LCCD) with both fine spatial and temporal resolutions from remote sensing images, subpixel mapping-based approaches have been widely studied in recent years. The fine spatial but coarse temporal resolution imag
Externí odkaz:
https://doaj.org/article/15f923d5ecc24f929aac0d16f524fd4d
Autor:
Jun Geng, Jing M. Chen, Weiliang Fan, Lili Tu, Yong Pang, Gang Yuan, Lichen Xu, Canyang Zhu, Teng Zhang, Chunju Zhang, Zhourun Ye, Yongchao Zhu, Zhenxuan Li
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 2901-2913 (2022)
The influence of tree distribution and slope on canopy gap fraction (GF) and bidirectional reflectance factor (BRF) is shown here to be non-negligible. Trees are often assumed to be randomly distributed in natural forests due to random distribution o
Externí odkaz:
https://doaj.org/article/f6c01cd663aa4520b309701c7294738c
Autor:
CHUNJU ZHANG
Publikováno v:
Scalable Computing: Practice & Experience; Jul2024, Vol. 25 Issue 4, p2175-2182, 8p
Autor:
Bing Xu, Chunju Zhang, Wencong Liu, Jianwei Huang, Yujiao Su, Yucheng Yang, Weijie Jiang, Wenhao Sun
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3407 (2023)
Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature inform
Externí odkaz:
https://doaj.org/article/b1a6a2dbd28a4be987f26133bc09e56f
Autor:
Weike Liu, Jing Xu, Huajing Song, Chunju Zhang, Yanli Yao, Hua Zhang, Yue-Chun Li, Zhendong Liu
Publikováno v:
Frontiers in Neurology, Vol 13 (2022)
ObjectiveSmall and dense low-density lipoprotein (sdLDL) elevation may be among the most sensitive early biomarkers for nascent cardiovascular disease. This study, therefore, investigated the association between visit-to-visit changes in sdLDL and ce
Externí odkaz:
https://doaj.org/article/b212af4a438545d7ab4e039a880027fd
Autor:
Runmin Lei, Chunju Zhang, Wencong Liu, Lei Zhang, Xueying Zhang, Yucheng Yang, Jianwei Huang, Zhenxuan Li, Zhiyi Zhou
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8297-8315 (2021)
Deep learning models have shown excellent performance in the hyperspectral remote sensing image (HSI) classification. In particular, convolutional neural networks (CNNs) have received widespread attention because of their powerful feature-extraction
Externí odkaz:
https://doaj.org/article/a9a521a9808244149eaffbdf54ae443f
Publikováno v:
Remote Sensing, Vol 15, Iss 3, p 617 (2023)
Classification algorithms integrated with convolutional neural networks (CNN) display high accuracies in synthetic aperture radar (SAR) image classification. However, their consideration of spatial information is not comprehensive and effective, whic
Externí odkaz:
https://doaj.org/article/8f1e7ca8a40d42698684d9cb640e1712