Vote for your favorite mineral in #MinCup25! - Chrysotile vs. Pectolite
Two new minerals to launch this year's cup! Hazardous (but useful!) chrysotile faces off against stunning gem pectolite.
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

Arun, Pattathal V., Herrmann, Ittai, Budhiraju, Krishna M., Karnieli, Arnon (2019) Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images. Pattern Recognition, 88. 431-446 doi:10.1016/j.patcog.2018.11.033

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleConvolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images
JournalPattern Recognition
AuthorsArun, Pattathal V.Author
Herrmann, IttaiAuthor
Budhiraju, Krishna M.Author
Karnieli, ArnonAuthor
Year2019 (April)Volume88
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2018.11.033Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367672Long-form Identifiermindat:1:5:9367672:0
GUID0
Full ReferenceArun, Pattathal V., Herrmann, Ittai, Budhiraju, Krishna M., Karnieli, Arnon (2019) Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images. Pattern Recognition, 88. 431-446 doi:10.1016/j.patcog.2018.11.033
Plain TextArun, Pattathal V., Herrmann, Ittai, Budhiraju, Krishna M., Karnieli, Arnon (2019) Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images. Pattern Recognition, 88. 431-446 doi:10.1016/j.patcog.2018.11.033
In(2019) Pattern Recognition Vol. 88. Elsevier BV

See Also

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

Arun, Pattathal V., Karnieli, Arnon (2021) Deep Learning-Based Phenological Event Modeling for Classification of Crops. Remote Sensing, 13 (13). doi:10.3390/rs13132477
Yang, Yue, Qi, Yong (2021) Image super-resolution via channel attention and spatial graph convolutional network. Pattern Recognition, 112. 107798pp. doi:10.1016/j.patcog.2020.107798
Sharma, Rahul, Deka, Bhabesh, Fusco, Vincent, Yurduseven, Okan (2024) Integrated convolutional neural networks for joint super-resolution and classification of radar images. Pattern Recognition, 150. 110351 doi:10.1016/j.patcog.2024.110351
Sun, Yubao, Chen, Jiwei, Liu, Qingshan, Liu, Guangcan (2020) Learning image compressed sensing with sub-pixel convolutional generative adversarial network. Pattern Recognition, 98. 107051pp. doi:10.1016/j.patcog.2019.107051
Salvoldi, Manuel, Tubul, Yaniv, Karnieli, Arnon, Herrmann, Ittai (2022) VENµS-Derived NDVI and REIP at Different View Azimuth Angles. Remote Sensing, 14 (1). doi:10.3390/rs14010184
Arun, Pattathal V.; Chaurasia, Kuldeep; Suresh, Soorya; Karnieli, Arnon (2025) Spatial Nonstationarity in DL-Based Crop Phenological Analysis. IEEE Geoscience and Remote Sensing Letters, 22. doi:10.1109/lgrs.2025.3558852
Pattathal V., Arun, Sahoo, Maitreya Mohan, Porwal, Alok, Karnieli, Arnon (2022) Deep-learning-based latent space encoding for spectral unmixing of geological materials. ISPRS Journal of Photogrammetry and Remote Sensing, 183. 307-320 doi:10.1016/j.isprsjprs.2021.11.008
Zhong, Yanfei, Zhang, Liangpei (2013) Sub-pixel mapping based on artificial immune systems for remote sensing imagery. Pattern Recognition, 46 (11). 2902-2926 doi:10.1016/j.patcog.2013.04.009
Liu, Yanbo, Hu, Yingxiang, Cao, Guo, Wang, Jin (2025) SENet: Super-resolution enhancement network for crowd counting. Pattern Recognition, 162. doi:10.1016/j.patcog.2025.111420
Chen, Rui, Zhang, Heng, Liu, Jixin (2022) Multi-attention augmented network for single image super-resolution. Pattern Recognition, 122. 108349pp. doi:10.1016/j.patcog.2021.108349
Hu, Quanwei; Tang, Yinggan; Bu, Chunning (2025) Dilated feature distillation attention network for efficient image super-resolution. Pattern Recognition, 168. doi:10.1016/j.patcog.2025.111856
 
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: September 1, 2025 18:38:21
Go to top of page