Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Jinxi QIAN"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13358-13375 (2024)
In recent years, deep-learning-based classification methods have been widely used for hyperspectral images (HSIs). However, in the existing transformer-based HSI classification methods, how to effectively and comprehensively utilize the rich informat
Externí odkaz:
https://doaj.org/article/df6c3721703949f4b82e84664a180fbb
Publikováno v:
Remote Sensing, Vol 16, Iss 6, p 990 (2024)
In hyperspectral image (HSI) classification scenarios, deep learning-based methods have achieved excellent classification performance, but often rely on large-scale training datasets to ensure accuracy. However, in practical applications, the acquisi
Externí odkaz:
https://doaj.org/article/ad7260d16ee34fc2812d46a967b8ce52
Publikováno v:
天地一体化信息网络, Vol 3, Pp 62-71 (2022)
A digital twin satellite internet (DTSI) architecture was proposed, which provided support for the whole life cycle of satellite internet through the three-dimensional closed-loop mechanism of parallel twin, security endogenous and intelligent evolut
Externí odkaz:
https://doaj.org/article/c2248326bdc94fb39077664d960ed4eb
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4391 (2023)
Deep learning has been demonstrated to be a powerful nonlinear modeling method with end-to-end optimization capabilities for hyperspectral Images (HSIs). However, in real classification cases, obtaining labeled samples is often time-consuming and lab
Externí odkaz:
https://doaj.org/article/e158d7effb554239b12c130ede064dc2
A weighted multiple-feature fusion classifier for hyperspectral images with limited training samples
Autor:
Jinghui Yang, Jinxi Qian
Publikováno v:
European Journal of Remote Sensing, Vol 51, Iss 1, Pp 1006-1021 (2018)
In this paper, a novel weighted multiple-feature classifier based on sparse representation and locally dictionary collaborative representation (WMSLC) is put forward to improve the limited training samples’ hyperspectral image classification perfor
Externí odkaz:
https://doaj.org/article/ff86975f68a0452a832467f8b474b50f
Autor:
Chendi Feng, Yuanfeng Li, Qi Zhang, Weiying Feng, Dong Chen, Jinxi Qian, Ying Tao, Feng Tian, Qinghua Tian
Publikováno v:
2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1392a91a2ed443d815bb891d343555d0
https://doi.org/10.1109/iccasit55263.2022.9986782
https://doi.org/10.1109/iccasit55263.2022.9986782
Autor:
Hui Li, Furong Chai, Dong Chen, Qi Zhang, Weiying Feng, Jinxi Qian, Ying Tao, Feng Tian, Qinghua Tian
Publikováno v:
2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e03f4e04a75bb1be33ae875664f67aa
https://doi.org/10.1109/iccasit55263.2022.9986677
https://doi.org/10.1109/iccasit55263.2022.9986677
Autor:
Jinghui Yang, Jinxi Qian
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 17:671-675
A novel hyperspectral image (HSI) classification method based on joint collaborative representation with shape adaptive region and locally adaptive dictionary (SALJCR) is proposed in this letter. First, the shape adaptive (SA) region is selected for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc334b48e70abee94022c2290c863343
https://doi.org/10.1109/lgrs.2019.2929840
https://doi.org/10.1109/lgrs.2019.2929840
Publikováno v:
2021 19th International Conference on Optical Communications and Networks (ICOCN).
An optimization scheme for Location-Assisted On-demand Routing protocol is proposed in order to improve its robustness. The simulation shows that proposed scheme can improve the robustness and reduce the routing cost of LEO routing system.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d277b97eae53d9e432a9f95ff3c049c
https://doi.org/10.1109/icocn53177.2021.9563794
https://doi.org/10.1109/icocn53177.2021.9563794
Autor:
Feng Tian, Dong Chen, Ying Tao, Qi Zhang, Yunxiao Zu, Cheng Ju, Mai Yang, Qinghua Tian, Jinxi Qian
Publikováno v:
2021 19th International Conference on Optical Communications and Networks (ICOCN).
In this paper, a resource reservation algorithm based on ARIMA model with balanced performance (RABP) is proposed. The algorithm uses Auto Regressive Integrated Moving Average (ARIMA) model to predict the change trend of future on-board traffic to re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d466562644de6c06273e582213afeaf1
https://doi.org/10.1109/icocn53177.2021.9563756
https://doi.org/10.1109/icocn53177.2021.9563756