Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Ziqiang Hua"'
Autor:
Ziqiang Hua, Yanling Liao, Jinxing Fu, Xinru Li, Qianxia Xu, Limin Lin, Meiling Huang, Bingmiao Gao
Publikováno v:
Marine Drugs, Vol 22, Iss 10, p 470 (2024)
The South China Sea is rich in sea anemone resources, and the protein and peptide components from sea anemone toxins comprise an important treasure trove for researchers to search for leading compounds. This study conducted a comprehensive transcript
Externí odkaz:
https://doaj.org/article/c4914e4613d443f68a4a4be3bc0da89a
Publikováno v:
Remote Sensing, Vol 15, Iss 23, p 5495 (2023)
Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs). Recently, deep learning-based BS methods have received widespread interest due to thei
Externí odkaz:
https://doaj.org/article/90214972245149cb83b6bf04327ae7b1
Publikováno v:
Remote Sensing, Vol 13, Iss 18, p 3602 (2021)
Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing. However, most of the existing BS methods fail to fully consider the interaction between spectral
Externí odkaz:
https://doaj.org/article/f7c8fd9452f64acfad2fc2f10269d368
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3147 (2021)
Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it diffi
Externí odkaz:
https://doaj.org/article/fd111c62a5c949ec88e93b44e0aca235
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Spatial information can play a supporting role in spectral unmixing. In this letter, we propose a dual branch autoencoder network to incorporate spatial-contextual information for spectral-spatial unmixing. The two branches leverage different archite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dc5bc2b7bc06d798d0bdbf3c81fc301
https://doi.org/10.1109/lgrs.2021.3091858
https://doi.org/10.1109/lgrs.2021.3091858
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9d8fc83cf167a52ab836d0a48cbe387
https://doi.org/10.1109/lgrs.2022.3178824
https://doi.org/10.1109/lgrs.2022.3178824
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 18:1640-1644
Autoencoder is an efficient technique for unsupervised feature learning, which can be applied to hyperspectral unmixing. In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous tec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e2cbdfa74eefaf9b55c7d3ce99ecba6
https://doi.org/10.1109/lgrs.2020.3005999
https://doi.org/10.1109/lgrs.2020.3005999
Publikováno v:
Remote Sensing, Vol 13, Iss 3602, p 3602 (2021)
Remote Sensing
Volume 13
Issue 18
Remote Sensing
Volume 13
Issue 18
Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing. However, most of the existing BS methods fail to fully consider the interaction between spectral
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5f2f0db66579061783b137038adb50c
https://www.mdpi.com/2072-4292/13/18/3602
https://www.mdpi.com/2072-4292/13/18/3602
Publikováno v:
Remote Sensing; Volume 13; Issue 16; Pages: 3147
Remote Sensing, Vol 13, Iss 3147, p 3147 (2021)
Remote Sensing, Vol 13, Iss 3147, p 3147 (2021)
Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it diffi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::049940ce82cb309940b963805afb7e8a
https://doi.org/10.3390/rs13163147
https://doi.org/10.3390/rs13163147
Publikováno v:
IGARSS
Spectral unmixing is a fundamental issue that needs to be addressed in the application of hyperspectral images. Due to the complex imaging conditions in remote sensing, it is common for the same object to have different spectral signatures. In this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5bc92aead54a15bcc01e03f966aa90ef
https://doi.org/10.1109/igarss.2019.8900529
https://doi.org/10.1109/igarss.2019.8900529