Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Dongen Guo"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15407-15419 (2024)
With the rapid development of deep learning (DL), change detection (CD) in remote sensing (RS) image has achieved remarkable success. Nevertheless, as the image resolution improves, the visual features extracted by current methods have limited expres
Externí odkaz:
https://doaj.org/article/d5168cc8e0194d6896f23614f04f9b3a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9569-9581 (2024)
Remote sensing image (RSI) target detection methods based on traditional multiscale feature fusion (MSFF) have achieved great success. However, the traditional MSFF method significantly increases the computational cost during model training and infer
Externí odkaz:
https://doaj.org/article/05e9b0acacef4dffb2f01664196d1763
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract In the field of object detection, feature pyramid network (FPN) can effectively extract multi-scale information. However, the majority of FPN-based methods suffer from a semantic gap between features of various sizes before feature fusion, w
Externí odkaz:
https://doaj.org/article/54286350cd784e4b8dfbfbe8b3261897
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-11 (2022)
Abstract With the availability of numerous high-resolution remote sensing images, remote sensing image scene classification has been widely used in various fields. Compared with the field of natural images, the insufficient number of labeled remote s
Externí odkaz:
https://doaj.org/article/6aac0ceda2ea409080ea8b77a9d7904d
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 2508-2521 (2021)
With the development of supervised deep neural networks, classification performance on existing remote sensing scene datasets has been markedly improved. However, supervised learning methods rely heavily on large-scale tagged examples to obtain a bet
Externí odkaz:
https://doaj.org/article/eb7142200a5f43abbf7a6e806a600ab0
Publikováno v:
IEEE Access, Vol 8, Pp 6344-6357 (2020)
Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly cont
Externí odkaz:
https://doaj.org/article/618e1948948e44e880df484c814ea68e
Publikováno v:
Applied Intelligence. 53:9056-9067
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4aab460edb6ed833bc23f9683e1e3add
https://doi.org/10.1007/s10489-022-03970-x
https://doi.org/10.1007/s10489-022-03970-x
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 18:2067-2071
With the advent of a large number of remote sensing images (RSIs), scene classification of RSI is widely applied to many fields such as urban planning, natural disaster detection, and environmental monitoring. Compared with the natural image field, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5dbc956b13ea704c97487d636e818a5
https://doi.org/10.1109/lgrs.2020.3014108
https://doi.org/10.1109/lgrs.2020.3014108
Autor:
Lei Lei, Dongen Guo
Publikováno v:
Computational Intelligence and Neuroscience
Computational Intelligence and Neuroscience, Vol 2021 (2021)
Computational Intelligence and Neuroscience, Vol 2021 (2021)
A remote sensing video satellite multiple object detection and tracking method based on road masking, Gaussian mixture model (GMM), and data association is proposed. This method first extracts the road network from the remote sensing video based on d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4174ca78ed9acfbaf027090ed95aab85
https://doi.org/10.1155/2021/7381909
https://doi.org/10.1155/2021/7381909
Publikováno v:
Neurocomputing. 443:117-130
Image super-resolution (SR) is a widely used and cost-effective technology in remote sensing image processing. Deep learning-based SR methods have shown promising performance, but they are prone to losing texture details. Instead, generative adversar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::607e0270f673fbb0314fe2b98d6fa458
https://doi.org/10.1016/j.neucom.2021.02.026
https://doi.org/10.1016/j.neucom.2021.02.026