Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Lingfei, Shi"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 935-948 (2025)
As climate change intensifies, extreme weather events like floods are occurring with increasing frequency. While data-driven deep learning methods are effective for extracting flood disaster information, their efficiency is constrained by the scarcit
Externí odkaz:
https://doaj.org/article/65cf23df1b7b4cf0952d4a4416a5a37e
Autor:
Lingfei Shi, Kun Yang
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
With the continuous enhancement of remote sensing data acquisition capabilities, the demand for efficient extraction of multi-class critical urban disaster information has become increasingly urgent. However, current deep learning-based models primar
Externí odkaz:
https://doaj.org/article/34a5dc7ef8504130aa092d3fb235d628
Autor:
Qiang Xu, Yulong Guo, Weiqiang Chen, Guangxing Ji, Lingfei Shi, Yuan Li, Zhubin Zheng, Chengzhi Sun, Huimin Zhu
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Monitoring rapidly changing inland water bodies using remote sensing requires a temporal resolution of at least 3 days and a spatial resolution of at least 30 meters. However, the current satellite data falls short of meeting these monitoring require
Externí odkaz:
https://doaj.org/article/2c00006cf6d34400adabdd3cadae3f43
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 2, Pp 3987-4007 (2023)
Different to pixel-based and object-based image recognition, a larger perspective based on the scene can improve the efficiency of assessing large-scale building damage. However, the complexity of disaster scenes and the scarcity of datasets are majo
Externí odkaz:
https://doaj.org/article/df5ab78d9a654caa8db672a9b8ccfb6b
Autor:
Yulong, Guo, Changchun, Huang, Yunmei, Li, Chenggong, Du, Lingfei, Shi, Yuan, Li, Weiqiang, Chen, Hejie, Wei, Enxiang, Cai, Guangxing, Ji
Publikováno v:
In Remote Sensing of Environment July 2022 276
Autor:
Yulong Guo, Changchun Huang, Yunmei Li, Chenggong Du, Yuan Li, Weiqiang Chen, Lingfei Shi, Guangxing Ji
Publikováno v:
Frontiers in Remote Sensing, Vol 3 (2022)
Due to strict spectral band requirements, the three-band (TB) chlorophyll-a concentration (Cchla) estimation algorithm cannot be applied to GOCI image, which has great potential in frequently monitoring inland complex waters. In this study, the TB al
Externí odkaz:
https://doaj.org/article/598ee7cb97344ec8908f5f4496cbb2dd
Autor:
Jiaqi Cui, Yulong Guo, Qiang Xu, Donghao Li, Weiqiang Chen, Lingfei Shi, Guangxing Ji, Ling Li
Publikováno v:
Agronomy, Vol 13, Iss 2, p 355 (2023)
Sudden flood disasters cause serious damage to agricultural production. Rapidly extracting information such as the flooding extent of agricultural land and capturing the influence of flooding on crops provides important guidelines for estimating the
Externí odkaz:
https://doaj.org/article/995a8292fdf647fbbcbc45924f7bf147
Autor:
Yulong Guo, Qingsheng Bi, Yuan Li, Chenggong Du, Junchang Huang, Weiqiang Chen, Lingfei Shi, Guangxing Ji
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7501 (2022)
Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performan
Externí odkaz:
https://doaj.org/article/c16b8652bb47437dbef22a085071ffc2
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4213 (2021)
The collapse of buildings caused by the earthquake seriously threatened human lives and safety. So, the quick detection of collapsed buildings from post-earthquake images is essential for disaster relief and disaster damage assessment. Compared with
Externí odkaz:
https://doaj.org/article/dce82f06ed23435999886ee9f1c3d7e6
Publikováno v:
Environmental Geochemistry and Health.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24f788d577bb1f87de1a747b95ecd46e
https://doi.org/10.1007/s10653-023-01526-7
https://doi.org/10.1007/s10653-023-01526-7