Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Wenxing Bao"'
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 54, Iss , Pp 101727- (2024)
Multimodal medical image fusion plays an important role in medical clinical applications. However, gradient features and intensity features are not extracted inadequately in fusion methods. To solve the above problems, this paper proposes a Gradient-
Externí odkaz:
https://doaj.org/article/20578d834cbf4aa9b7d91c7de05c9cd3
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9628-9644 (2024)
The purpose of hyperspectral unmixing (HU) is to extract the spectral signatures and their proportion fractions from the hyperspectral remote sensing image (HSIs), which is a crucial issue in HSIs processing. Recently, nonnegative tensor factorizatio
Externí odkaz:
https://doaj.org/article/de5916551f184792ae306aa1cc418cfa
Autor:
Yang Cao, Wei Feng, Yinghui Quan, Wenxing Bao, Gabriel Dauphin, Yijia Song, Aifeng Ren, Mengdao Xing
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 409-418 (2023)
Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classifi
Externí odkaz:
https://doaj.org/article/3e9050ea04b64d73b58adaf44d802a2e
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 4983 (2023)
This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution. This algorithm seamlessly integrates graph re
Externí odkaz:
https://doaj.org/article/118c7b21bee24d5280a5fcac81fa0a7d
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4610 (2023)
Multispectral and hyperspectral image fusion (MHF) aims to reconstruct high-resolution hyperspectral images by fusing spatial and spectral information. Unlike the traditional canonical polyadic decomposition and Tucker decomposition models, the block
Externí odkaz:
https://doaj.org/article/9759e26e67dc4913ba3b846011f20171
Autor:
Shuo Wang, Wei Feng, Yinghui Quan, Wenxing Bao, Gabriel Dauphin, Lianru Gao, Xian Zhong, Mengdao Xing
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5943-5952 (2022)
Hyperspectral images (HSIs) have always played an important role in remote sensing applications. Anomaly detection has become a hot spot in HSI processing in recent years. The popular detecting method is to accurately segment anomalies from the backg
Externí odkaz:
https://doaj.org/article/f051d035b9264f618d8ad42b4cb82dda
Autor:
Yang Cao, Wei Feng, Yinghui Quan, Wenxing Bao, Gabriel Dauphin, Aifeng Ren, Xiaoguang Yuan, Mengdao Xing
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 7375-7390 (2022)
Remote sensing image change detection is the key technology for monitoring forest windfall damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection
Externí odkaz:
https://doaj.org/article/558a6a9c8ee54c0eb9ff15132a1c3d31
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 3386-3402 (2021)
Endmember extraction algorithms (EEAs) play a crucial role in hyperspectral image (HSI) perception, and yet they normally suffer from three flaws: 1) High computational burden, 2) weak noise robustness, and 3) high outlier sensitivity. To solve these
Externí odkaz:
https://doaj.org/article/5d60804e54da4ddfaeb46eb54c98e3ad
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 10017-10032 (2021)
Recent research shows that generative adversarial network (GAN) based deep learning derived frameworks can improve the accuracy of hyperspectral image (HSI) classification on limited labeled samples. However, several studies point out that existing G
Externí odkaz:
https://doaj.org/article/b7a30adf85374d5c9caf791956731851
Autor:
Kewen Qu, Wenxing Bao
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 963-975 (2020)
Nonnegative matrix factorization (NMF) is widely used in unmixing issue in recent years, because it can simultaneously estimate the endmembers and abundances. However, most existing NMF-based methods only consider single matrix constraints and the ot
Externí odkaz:
https://doaj.org/article/92322216858f49a285b81001b67a7b76