Výsledky vyhledávání - "Kaiwen Zhong"

Akademický článek

Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature

Autor: Pinghao Wu, Jiacheng Liang, Jianhui Xu, Kaiwen Zhong, Hongda Hu, Jian Zuo

Publikováno v: Atmosphere, Vol 14, Iss 12, p 1813 (2023)

This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deepla

Externí odkaz: https://doaj.org/article/5b099e4804e3414e8040b1923bd03945

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Akademický článek

Classification of High-Resolution Remote Sensing Images in the Feilaixia Reservoir Based on a Fully Convolutional Network

Autor: Pinghao Wu, Kaiwen Zhong, Hongda Hu, Jianhui Xu, Yunpeng Wang, Yi Zhao

Publikováno v: IEEE Access, Vol 8, Pp 161752-161764 (2020)

In technologically underdeveloped areas, water pollution threatens the living environment of local residents, so remote sensing monitoring of the features around reservoirs is necessary. Fully convolutional networks (FCNs) offer great potential for e

Externí odkaz: https://doaj.org/article/b42aa3e43f2c477a9e07dfe0be91839f

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Akademický článek

Impervious Surface Extraction by Linear Spectral Mixture Analysis with Post-Processing Model

Autor: Yi Zhao, Jianhui Xu, Kaiwen Zhong, Yunpeng Wang, Hongda Hu, Pinghao Wu

Publikováno v: IEEE Access, Vol 8, Pp 128476-128489 (2020)

Accurate estimations of impervious surface areas are essential for urban planning development. Linear spectral mixture analysis (LSMA) is commonly adopted to extract the impervious surface (IS) fraction in a mixed pixel at the subpixel scale. However

Externí odkaz: https://doaj.org/article/94668a1a333040ac83fd7ef2fc20ea47

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Akademický článek

Long-term offshore wind power prediction using spatiotemporal kriging: A case study in China’s Guangdong Province

Autor: Hongda Hu, Zhiyong Hu, Kaiwen Zhong, Jianhui Xu, Pinghao Wu, Yi Zhao, Feifei Zhang

Publikováno v: Energy Exploration & Exploitation, Vol 38 (2020)

The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies ha

Externí odkaz: https://doaj.org/article/b1d10e72422141e7921dc888d759e5a8

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Akademický článek

Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

Autor: Jianhui Xu, Yi Zhao, Caige Sun, Hanbin Liang, Ji Yang, Kaiwen Zhong, Yong Li, Xulong Liu

Publikováno v: Remote Sensing, Vol 13, Iss 5, p 1019 (2021)

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou f

Externí odkaz: https://doaj.org/article/7fab294cae49497d93628c056738787b

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Akademický článek

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

Autor: Jianhui Xu, Feifei Zhang, Hao Jiang, Hongda Hu, Kaiwen Zhong, Wenlong Jing, Ji Yang, Binghao Jia

Publikováno v: Remote Sensing, Vol 12, Iss 7, p 1082 (2020)

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolutio

Externí odkaz: https://doaj.org/article/b77f6b527da54ae9b2f93662caae47dc

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Akademický článek

Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China

Autor: Jianhui Xu, Yi Zhao, Kaiwen Zhong, Huihua Ruan, Xulong Liu

Publikováno v: Water, Vol 8, Iss 12, p 550 (2016)

Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA)

Externí odkaz: https://doaj.org/article/b5c5e364c2ab4d109d7ffcb1d43780c4

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