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

Puttinaovarat, Supattra, Horkaew, Paramate (2019) Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation. Earth Science Informatics, 12 (4) 429-446 doi:10.1007/s12145-019-00387-y

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleDeep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation
JournalEarth Science Informatics
AuthorsPuttinaovarat, SupattraAuthor
Horkaew, ParamateAuthor
Year2019 (December)Volume12
Issue4
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s12145-019-00387-ySearch in ResearchGate
Generate Citation Formats
Mindat Ref. ID729863Long-form Identifiermindat:1:5:729863:3
GUID0
Full ReferencePuttinaovarat, Supattra, Horkaew, Paramate (2019) Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation. Earth Science Informatics, 12 (4) 429-446 doi:10.1007/s12145-019-00387-y
Plain TextPuttinaovarat, Supattra, Horkaew, Paramate (2019) Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation. Earth Science Informatics, 12 (4) 429-446 doi:10.1007/s12145-019-00387-y
In(2019, December) Earth Science Informatics Vol. 12 (4) Springer Science and Business Media LLC

See Also

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

Puttinaovarat, Supattra, Horkaew, Paramate (2018) Multi-spectral and Topographic Fusion for Automated Road Extraction. Open Geosciences, 10 (1). 461-473 doi:10.1515/geo-2018-0036
Puttinaovarat, Supattra, Horkaew, Paramate (2022) A Geospatial Platform for Crowdsourcing Green Space Area Management Using GIS and Deep Learning Classification. ISPRS International Journal of Geo-Information, 11 (3) 208pp. doi:10.3390/ijgi11030208
Horkaew, Paramate, Puttinaovarat, Supattra (2017) Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring. ISPRS International Journal of Geo-Information, 6. 301pp. doi:10.3390/ijgi6100301
Puttinaovarat, Supattra, Chai-Arayalert, Supaporn, Saetang, Wanida (2024) Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization. ISPRS International Journal of Geo-Information, 13 (5) 158 doi:10.3390/ijgi13050158
Shi, Wenzao, Mao, Zhengyuan (2016) Building extraction from panchromatic high-resolution remotely sensed imagery based on potential histogram and neighborhood Total variation. Earth Science Informatics, 9 (4) 497-509 doi:10.1007/s12145-016-0262-6
Yusefi, Amir, Ramazi, Hamidreza (2019) ProxInLAS, a software program for detecting coal layers and estimating parameters of layers, using geophysical well-logs. Earth Science Informatics, 12 (3) 415-427 doi:10.1007/s12145-019-00382-3
Zhou, Chen, Li, Manchun (2019) Improving and evaluating boundary algebra filling for identifying polygon intersections. Earth Science Informatics, 12 (4) 581-597 doi:10.1007/s12145-019-00405-z
Evrendilek, F., Karakaya, N. (2015) Spatiotemporal modeling of saturated dissolved oxygen through regressions after wavelet denoising of remotely and proximally sensed data. Earth Science Informatics, 8 (1) 247-254 doi:10.1007/s12145-014-0148-4
Sermet, Yusuf, Demir, Ibrahim (2019) Towards an information centric flood ontology for information management and communication. Earth Science Informatics, 12 (4) 541-551 doi:10.1007/s12145-019-00398-9
Khoramak, Sahar, Mahmoudi, Fatemeh Tabib (2023) Multi-agent hyperspectral and lidar features fusion for urban vegetation mapping. Earth Science Informatics, 16 (1) 165-173 doi:10.1007/s12145-022-00928-y
Zhao, Zhihong, He, Peng (2024) YOLO-U: multi-task model for vehicle detection and road segmentation in UAV aerial imagery. Earth Science Informatics, 17 (4). doi:10.1007/s12145-024-01335-1
 
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 Β© 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 05:53:11
Go to top of page