Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Zhitong Xiong"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4833-4845 (2023)
We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameter estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the
Externí odkaz:
https://doaj.org/article/b639b1e58ce4429293c95c5c961fd163
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
Publikováno v:
IEEE Access, Vol 7, Pp 106739-106747 (2019)
RGB-D image-based scene recognition has achieved significant performance improvement with the development of deep learning methods. While convolutional neural networks can learn high-semantic level features for object recognition, these methods still
Externí odkaz:
https://doaj.org/article/e012691d29fd4667b6d05d3cf26b540a
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 61:1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e39929823fc6a97bec1218533067cee2
https://doi.org/10.1109/tgrs.2023.3267271
https://doi.org/10.1109/tgrs.2023.3267271
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 195:192-203
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee674dc7f12f40ae8042cb3bad3c01cf
https://doi.org/10.1016/j.isprsjprs.2022.11.013
https://doi.org/10.1016/j.isprsjprs.2022.11.013
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:7551-7562
Binarized neural networks (BNNs) have drawn significant attention in recent years, owing to great potential in reducing computation and storage consumption. While it is attractive, traditional BNNs usually suffer from slow convergence speed and drama
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ef3509699d08fee543cb6420983b6d7
https://doi.org/10.1109/tpami.2021.3117908
https://doi.org/10.1109/tpami.2021.3117908
Autor:
Zhitong Xiong, Xiao Xiang Zhu
Earth observation (EO) data are critical for monitoring the state of planet Earth and can be helpful for various real-world applications [1]. Although numerous benchmark datasets have been released, there is no unified platform for developing and fai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::132cf994a5d224d6d7bd0ee7e039cb0b
https://doi.org/10.5194/egusphere-egu23-3501
https://doi.org/10.5194/egusphere-egu23-3501
Precipitation nowcasting, aiming to predict the rainfall intensity in the near future (usually 0-2h) [1], is crucial for urban planning, flood monitoring, agriculture management, and so on. Numerical weather modeling (NWP) takes a variety of data sou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::773e3754552c48b2b7bdeb1d4556ea69
https://doi.org/10.5194/egusphere-egu23-7751
https://doi.org/10.5194/egusphere-egu23-7751
Soil parameters are relevant and valuable for various applications such as agriculture production, scientific research, and policy making. Since acquiring such physical or chemical information could be cost-consuming by traditional methods, remote se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c51a9546ab4d422d89fc06166a1215de
https://doi.org/10.5194/egusphere-egu23-2439
https://doi.org/10.5194/egusphere-egu23-2439
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:1414-1428
Remote sensing image scene classification has attracted great attention because of its wide applications. Although convolutional neural network (CNN)-based methods for scene classification have achieved excellent results, the large-scale variation of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af70cd58f87b347cd068e5ca29a13211
https://doi.org/10.1109/tnnls.2020.3042276
https://doi.org/10.1109/tnnls.2020.3042276