Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Runcheng Jiao"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11086-11100 (2024)
The complex terrain and abundant ravines in the western mountainous areas of Beijing have led to dramatic changes in the geological environment. Monitoring and assessing the stability of landslide surfaces is of great significance for disaster preven
Externí odkaz:
https://doaj.org/article/3615c2e2e2f2433c833eb93a310c1894
Autor:
Jianfeng Han, Xuefei Guo, Runcheng Jiao, Yun Nan, Honglei Yang, Xuan Ni, Danning Zhao, Shengyu Wang, Xiaoxue Ma, Chi Yan, Chi Ma, Jia Zhao
Publikováno v:
Remote Sensing, Vol 15, Iss 17, p 4287 (2023)
InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation method
Externí odkaz:
https://doaj.org/article/d2ab11d5337340f69a06a6879371f178
Publikováno v:
Zhongguo dizhi zaihai yu fangzhi xuebao, Vol 32, Iss 1, Pp 70-76 (2021)
Analytic hierarchy process (AHP) is one of the important methods for the comprehensive evaluation of geological environment quality.Evaluation index system is the key to evaluate success or failure. In the current research, a single evaluation index
Externí odkaz:
https://doaj.org/article/02918867260e45d28b7c73454c969dfd
Publikováno v:
Remote Sensing, Vol 14, Iss 20, p 5067 (2022)
Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-tim
Externí odkaz:
https://doaj.org/article/e4c2ced6584542d09063cc811e138813
Publikováno v:
Remote Sensing, Vol 14, Iss 19, p 4695 (2022)
The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region’s de
Externí odkaz:
https://doaj.org/article/2f0f2b2425a14a6ca95f7efdef926fa0
Autor:
Xianchuan Yu, Yuanfei Zhang, Wang Yao, Ying Zhan, Wei Liu, Ying Cao, Jin Qin, Yuntao Wang, Kang Wu, Xi Zhang, Cong Dai, Dan Hu, RunCheng Jiao, Yasmine Medjadba
Publikováno v:
IGARSS
Hyperspectral data contains abundant information in spectral domain, which is very useful for mineral classification and geological body mapping. But, due to the lack of labeled data, it is difficult to get an acceptable result by just using the smal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb3f4ea8372c7d4bb9caef2ab94ee325
https://doi.org/10.1109/igarss.2019.8900335
https://doi.org/10.1109/igarss.2019.8900335
Autor:
Xianchuan Yu, Yuntao Wang, Yasmine Medjadba, Ying Cao, Jin Qin, Kang Wu, Zhengang Zhao, Guian Wang, Dan Hu, Ying Zhan, RunCheng Jiao, Tao Huang
Publikováno v:
IGARSS
Classifying Hyperspectral images with few training samples is a challenging problem. The generative adversarial networks (GAN) are promising techniques to address the problems. GAN constructs an adversarial game between a discriminator and a generato
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22cf073cb4b1e47c447c8e8bb2155a79
https://doi.org/10.1109/igarss.2019.8899291
https://doi.org/10.1109/igarss.2019.8899291