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

Zhang, Jianting, Gruenwald, Le, Gertz, Michael (2009) VDM-RS: A visual data mining system for exploring and classifying remotely sensed images. Computers & Geosciences, 35 (9) 1827-1836 doi:10.1016/j.cageo.2009.02.006

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleVDM-RS: A visual data mining system for exploring and classifying remotely sensed images
JournalComputers & Geosciences
AuthorsZhang, JiantingAuthor
Gruenwald, LeAuthor
Gertz, MichaelAuthor
Year2009 (September)Volume35
Issue9
PublisherElsevier BV
DOIdoi:10.1016/j.cageo.2009.02.006Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID451605Long-form Identifiermindat:1:5:451605:1
GUID0
Full ReferenceZhang, Jianting, Gruenwald, Le, Gertz, Michael (2009) VDM-RS: A visual data mining system for exploring and classifying remotely sensed images. Computers & Geosciences, 35 (9) 1827-1836 doi:10.1016/j.cageo.2009.02.006
Plain TextZhang, Jianting, Gruenwald, Le, Gertz, Michael (2009) VDM-RS: A visual data mining system for exploring and classifying remotely sensed images. Computers & Geosciences, 35 (9) 1827-1836 doi:10.1016/j.cageo.2009.02.006
In(2009, September) Computers & Geosciences Vol. 35 (9) Elsevier BV

See Also

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

Zhou, Yu, Fung, Tung, Leung, Yee (2016) Improved triangular prism methods for fractal analysis of remotely sensed images. Computers & Geosciences, 90. 64-77 doi:10.1016/j.cageo.2016.02.018
Jarvis, C.H., Stuart, N. (1996) The sensitivity of a neural network for classifying remotely sensed imagery. Computers & Geosciences, 22 (9) 959-967 doi:10.1016/s0098-3004(96)00034-9
Warren, M.A., Goult, S., Clewley, D. (2018) The Simple Concurrent Online Processing System (SCOPS) - An open-source interface for remotely sensed data processing. Computers & Geosciences, 115. 188-197 doi:10.1016/j.cageo.2018.03.013
Li, Jiyuan, Meng, Lingkui, Wang, Frank Z., Zhang, Wen, Cai, Yang (2014) A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation. Computers & Geosciences, 70. 110-119 doi:10.1016/j.cageo.2014.05.008
Yunhao, Chen, Peijun, Shi, Xiaobing, Li, Jin, Chen, Jing, Li (2006) A combined approach for estimating vegetation cover in urban/suburban environments from remotely sensed data. Computers & Geosciences, 32 (9) 1299-1309 doi:10.1016/j.cageo.2005.11.011
Balaguer-Beser, A., Ruiz, L.A., Hermosilla, T., Recio, J.A. (2013) Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images. Computers & Geosciences, 50. 115-127 doi:10.1016/j.cageo.2012.08.001
Warren, Mark A., Taylor, Benjamin H., Grant, Michael G., Shutler, Jamie D. (2014) Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment. Computers & Geosciences, 64. 24-34 doi:10.1016/j.cageo.2013.11.006
Beran, Bora, Piasecki, Michael (2009) Engineering new paths to water data. Computers & Geosciences, 35 (4) 753-760 doi:10.1016/j.cageo.2008.02.017
Milton, E.J., Baret, F., Rossello, P., Anderson, E., Rockall, E. (2011) A multistage database of field measurements and synoptic remotely sensed data to support model validation and testing in Earth observation. Computers & Geosciences, 37 (9) 1511-1514 doi:10.1016/j.cageo.2011.02.007
Zhang, Quanfa, Pavlic, Goran, Chen, Wenjun, Fraser, Robert, Leblanc, Sylvain, Cihlar, Josef (2005) A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery. Computers & Geosciences, 31 (3) 289-296 doi:10.1016/j.cageo.2004.10.003
Bernabé, Sergio, Plaza, Antonio, Reddy Marpu, Prashanth, Atli Benediktsson, Jon (2012) A new parallel tool for classification of remotely sensed imagery. Computers & Geosciences, 46. 208-218 doi:10.1016/j.cageo.2011.12.009
 
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 17, 2025 19:46:21
Go to top of page