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

Cánovas-García, Fulgencio, Alonso-Sarría, Francisco (2015) Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery. Remote Sensing, 7 (4). doi:10.3390/rs70404651

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleOptimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery
JournalRemote Sensing
AuthorsCánovas-García, FulgencioAuthor
Alonso-Sarría, FranciscoAuthor
Year2015 (April 17)Volume7
Issue4
PublisherMDPI AG
DOIdoi:10.3390/rs70404651Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17804562Long-form Identifiermindat:1:5:17804562:3
GUID0
Full ReferenceCánovas-García, Fulgencio, Alonso-Sarría, Francisco (2015) Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery. Remote Sensing, 7 (4). doi:10.3390/rs70404651
Plain TextCánovas-García, Fulgencio, Alonso-Sarría, Francisco (2015) Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery. Remote Sensing, 7 (4). doi:10.3390/rs70404651
In(2015, April) Remote Sensing Vol. 7 (4). MDPI AG

See Also

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

Gomariz-Castillo, Francisco, Alonso-Sarría, Francisco, Cánovas-García, Fulgencio (2017) Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015. Remote Sensing, 9 (10). doi:10.3390/rs9101058
Cánovas-García, Fulgencio, García-Galiano, Sandra, Alonso-Sarría, Francisco (2018) Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula. Remote Sensing, 10 (7). doi:10.3390/rs10071023
Cánovas-García, Fulgencio, Alonso-Sarría, Francisco, Gomariz-Castillo, Francisco, Oñate-Valdivieso, Fernando (2017) Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery. Computers & Geosciences, 103. 1-11 doi:10.1016/j.cageo.2017.02.012
Baudron, Paul, Alonso-Sarría, Francisco, García-Aróstegui, José Luís, Cánovas-García, Fulgencio, Martínez-Vicente, David, Moreno-Brotóns, Jesús (2013) Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification. Journal of Hydrology, 499. 303-315 doi:10.1016/j.jhydrol.2013.07.009
Yang, Lingbo, Mansaray, Lamin, Huang, Jingfeng, Wang, Limin (2019) Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery. Remote Sensing, 11 (5). doi:10.3390/rs11050514
Zhang, Xin, Han, Liangxiu, Han, Lianghao, Zhu, Liang (2020) How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery? Remote Sensing, 12 (3). doi:10.3390/rs12030417
Cánovas‐García, Fulgencio, Pesántez‐Cobos, Paúl, Alonso‐Sarría, Francisco (2020) fusionImage: An R package for pan‐sharpening images in open source software. Transactions in GIS, 24 (5) 1185-1207 doi:10.1111/tgis.12676
Gu, Haiyan, Li, Haitao, Yan, Li, Liu, Zhengjun, Blaschke, Thomas, Soergel, Uwe (2017) An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology. Remote Sensing, 9 (4). doi:10.3390/rs9040329
Liu, Xiaolong, Bo, Yanchen (2015) Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data. Remote Sensing, 7 (1). doi:10.3390/rs70100922
VANCOILLIE, F, VERBEKE, L, DEWULF, R (2007) Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium. Remote Sensing of Environment, 110 (4). 476-487 doi:10.1016/j.rse.2007.03.020
Dribault, Yann, Chokmani, Karem, Bernier, Monique (2012) Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification. Remote Sensing, 4 (7). doi:10.3390/rs4071887
 
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 29, 2025 18:50:39
Go to top of page