Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Liu, Shuang, Fu, Chuan, Duan, Yule, Wang, Xiaopan, Luo, Fulin (2025) Spatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples. IEEE Transactions on Geoscience and Remote Sensing, 63. doi:10.1109/tgrs.2024.3523578

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleSpatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples
JournalIEEE Transactions on Geoscience and Remote Sensing
AuthorsLiu, ShuangAuthor
Fu, ChuanAuthor
Duan, YuleAuthor
Wang, XiaopanAuthor
Luo, FulinAuthor
Year2025Volume63
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
DOIdoi:10.1109/tgrs.2024.3523578Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17963695Long-form Identifiermindat:1:5:17963695:0
GUID0
Full ReferenceLiu, Shuang, Fu, Chuan, Duan, Yule, Wang, Xiaopan, Luo, Fulin (2025) Spatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples. IEEE Transactions on Geoscience and Remote Sensing, 63. doi:10.1109/tgrs.2024.3523578
Plain TextLiu, Shuang, Fu, Chuan, Duan, Yule, Wang, Xiaopan, Luo, Fulin (2025) Spatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples. IEEE Transactions on Geoscience and Remote Sensing, 63. doi:10.1109/tgrs.2024.3523578
In(2025) IEEE Transactions on Geoscience and Remote Sensing Vol. 63. Institute of Electrical and Electronics Engineers (IEEE)

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

Not Yet Imported: - journal-article : 10.1109/TCYB.2021.3070577

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TBDATA.2019.2923243

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: IEEE Transactions on Image Processing - journal-article : 10.1109/TIP.2016.2601268

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TPAMI.2005.127

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1007/s11554-016-0650-7

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TCYB.2020.2977750

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TIP.2023.3244414

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Gu (2023) arXiv:2312.00752 Mamba: Linear-time sequence modeling with selective state spaces
Gu () Proc. Int. Conf. Learn. Represent. Efficiently modeling long sequences with structured state spaces , 1
Not Yet Imported: IEEE Transactions on Neural Networks and Learning Systems - journal-article : 10.1109/TNNLS.2022.3182715

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TCSVT.2022.3218284

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Not Yet Imported: - journal-article : 10.1109/TNNLS.2022.3198142

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.neucom.2021.03.035

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: IEEE Transactions on Geoscience and Remote Sensing - journal-article : 10.1109/TGRS.2022.3197445

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TIP.2017.2772836

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.3390/rs14246308

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.3390/s19071714

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: IEEE Transactions on Neural Networks and Learning Systems - journal-article : 10.1109/TNNLS.2018.2874432

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Zhu (2024) arXiv:2401.09417 Vision mamba: Efficient visual representation learning with bidirectional state space model
Not Yet Imported: - journal-article : 10.1145/3582688

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Not Yet Imported: - journal-article : 10.1016/j.jksuci.2023.01.014

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Srivastava (2014) J. Mach. Learn. Res. Dropout: A simple way to prevent neural networks from overfitting 15, 1929
Not Yet Imported: - journal-article : 10.1016/j.protcy.2012.10.074

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1609/aaai.v30i1.10171

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.knosys.2021.107279

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - proceedings-article : 10.1117/12.943611

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1109/TGRS.2022.3207933

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Kingma (2014) arXiv:1412.6980 Adam: A method for stochastic optimization
Not Yet Imported: - journal-article : 10.1109/JSTARS.2021.3099118

If you would like this item imported into the Digital Library, please contact us quoting Journal ID


See Also

These are possibly similar items as determined by title/reference text matching only.

 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 21, 2025 16:30:48
Go to top of page