Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Wenchao Kang"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2133-2148 (2024)
In recent years, change detection (CD) of optical remote sensing images has made remarkable progress through using deep learning. However, current CD deep learning methods are usually improved from the semantic segmentation models, and focus on enhan
Externí odkaz:
https://doaj.org/article/10ac4d09992c4e48b5380843c5fff4ec
Autor:
Bihua Zhang, Wenchao Kang, Shiqiang Xiong, Xia Huang, Pei Chen, Jinmin Huang, Yufei Hou, Lin Ma, Tao Xiang
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Abstract The coronavirus disease 2019 (COVID-19) brought a healthy crisis all around the world. It’s not only affected healthcare utilization, but also some disease’s incidence. We collected pre-hospital emergency data in Chengdu from January 201
Externí odkaz:
https://doaj.org/article/74c62d5b1ba14f73920b2eae9fb04735
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 1562-1574 (2022)
As two of the most widely used remote sensing images, optical and synthetic aperture radar (SAR) images show abundant and complementary information on the same target owing to their individual imaging mechanisms. Consequently, using optical and SAR i
Externí odkaz:
https://doaj.org/article/4fb815067c9a43b4a204c835dec54ed9
Publikováno v:
Sensors, Vol 18, Iss 9, p 2915 (2018)
Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for floo
Externí odkaz:
https://doaj.org/article/61778b6ec89d480aa4580355d7a1c442
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Land cover classification is the basic task of remote sensing image interpretation. Related methods have developed rapidly, especially the branch based on deep learning (DL). For high-resolution remote sensing images, the smaller inter-class differen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b0b1da08361e6ba906b2e5836ad57de
https://doi.org/10.1109/lgrs.2021.3114305
https://doi.org/10.1109/lgrs.2021.3114305
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea32acea0c9d4b2cbd4165951b1a4ff2
https://doi.org/10.1109/tgrs.2022.3213925
https://doi.org/10.1109/tgrs.2022.3213925
Publikováno v:
IGARSS
China is a flood disaster-prone country, floods occur almost every year, especially in July and August. Rapid detection and assessment for floods affected areas are of great significance. The Chinese GF-3 SAR satellite, which uses active ground obser
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de3cf6031f3193c9a40f5151ab1aa563
https://doi.org/10.1109/igarss.2018.8517886
https://doi.org/10.1109/igarss.2018.8517886
Publikováno v:
Remote Sensing; Volume 11; Issue 23; Pages: 2813
Automatic building extraction from high-resolution remote sensing images has many practical applications, such as urban planning and supervision. However, fine details and various scales of building structures in high-resolution images bring new chal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d41e7abc949a37383330057c9f9d96ab
https://doi.org/10.3390/rs11232813
https://doi.org/10.3390/rs11232813
Publikováno v:
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; 2017, Vol. 42 Issue 2/W7, p651-654, 4p
Publikováno v:
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; 2017, Vol. 42 Issue 2/W7, p605-608, 4p