Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Lichao Mou"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17860-17875 (2024)
Deep learning techniques have shown the capability in GNSS reflectometry (GNSS-R) for retrieving geographical parameters based on GNSS-R observations. Recent studies have proved that such data-driven approaches can significantly improve the quality o
Externí odkaz:
https://doaj.org/article/214f7062a03844c694d47d23385b1da7
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8509-8526 (2024)
In recent years, deep learning has emerged as the dominant approach for hyperspectral image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learning for re
Externí odkaz:
https://doaj.org/article/9acd871592104ce393434ac0250bd173
Publikováno v:
Frontiers in Remote Sensing, Vol 4 (2023)
Externí odkaz:
https://doaj.org/article/5ec2b109abc04c30b828fb37b5a1e0eb
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 123, Iss , Pp 103491- (2023)
The function of plastics is an important issue, especially since it determines whether or not they can be recycled. This study presents a two-stage workflow to identify the functions of plastic materials on land surfaces using a deep learning model t
Externí odkaz:
https://doaj.org/article/a92eb6a257e34463b5fdccebe3b9e502
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 122, Iss , Pp 103358- (2023)
This paper describes a deep transfer model which consists of multiple sub-networks which are independently optimized by a supervised task-oriented loss and an unsupervised consistency loss. The former loss function utilizes annotations to accomplish
Externí odkaz:
https://doaj.org/article/46fd317b50fc4364b0293589e71d021d
Autor:
Runmin Dong, Lixian Zhang, Weijia Li, Shuai Yuan, Lin Gan, Juepeng Zheng, Haohuan Fu, Lichao Mou, Xiao Xiang Zhu
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 121, Iss , Pp 103381- (2023)
Sentinel-2 imagery has garnered significant attention in many earth system studies due to free access and high revisit frequency. Since its spatial resolution is insufficient for many applications, e.g., fine-grained land cover mapping, some studies
Externí odkaz:
https://doaj.org/article/12987818fb404a8ea4c20ee84bd12515
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 120, Iss , Pp 103311- (2023)
Three-dimensional (3D) building structures are vital to understanding urban dynamics. Monocular remote sensing imagery is a cost-effective data source for large-scale building height retrieval when compared to LiDAR data and multi-view imagery. Exist
Externí odkaz:
https://doaj.org/article/b3a7d5ab400f4f9f99c8040cb4c4c136
Autor:
Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, Xiao Xiang Zhu
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 116, Iss , Pp 103130- (2023)
Many deep learning approaches make extensive use of backbone networks pretrained on large datasets like ImageNet, which are then fine-tuned. In remote sensing, the lack of comparable large annotated datasets and the diversity of sensing platforms imp
Externí odkaz:
https://doaj.org/article/7e59733d574f49a7a91a10630abec717
Publikováno v:
Remote Sensing, Vol 10, Iss 10, p 1572 (2018)
Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ m
Externí odkaz:
https://doaj.org/article/48ad39d111ea4de98c741cc257075265
Autor:
Haobo Lyu, Hui Lu, Lichao Mou, Wenyu Li, Jonathon Wright, Xuecao Li, Xinlu Li, Xiao Xiang Zhu, Jie Wang, Le Yu, Peng Gong
Publikováno v:
Remote Sensing, Vol 10, Iss 3, p 471 (2018)
Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectr
Externí odkaz:
https://doaj.org/article/e5c6003711ea4a01912c20c0b588b8f4