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dc.contributor.authorAksoy, Selimen_US
dc.contributor.authorKoperski, K.en_US
dc.contributor.authorTusk, C.en_US
dc.contributor.authorMarchisio G.en_US
dc.coverage.spatialSeattle, WA, USAen_US
dc.date.accessioned2016-02-08T11:53:04Z
dc.date.available2016-02-08T11:53:04Z
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/11693/27426
dc.descriptionDate of Conference: August 22 - 25, 2004en_US
dc.description.abstractAdvances in satellite technology and availability of down-loaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives.en_US
dc.language.isoEnglishen_US
dc.source.titleKDD '04 Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data miningen_US
dc.relation.isversionofhttps://doi.org/10.1145/1014052.1016913en_US
dc.subjectData fusionen_US
dc.subjectDecision tree classifiersen_US
dc.subjectLand cover analysisen_US
dc.subjectMissing dataen_US
dc.subjectRemote sensingen_US
dc.subjectArtificial intelligenceen_US
dc.subjectClassification (of information)en_US
dc.subjectData transferen_US
dc.subjectDatabase systemsen_US
dc.subjectGeographic information systemsen_US
dc.subjectImage analysisen_US
dc.subjectImage retrievalen_US
dc.subjectMaximum likelihood estimationen_US
dc.subjectPattern recognitionen_US
dc.subjectRemote sensingen_US
dc.subjectSensor data fusionen_US
dc.subjectDecision tree classifiersen_US
dc.subjectImage archivesen_US
dc.subjectLand cover analysisen_US
dc.subjectMissing dataen_US
dc.subjectInteractive computer systemsen_US
dc.titleInteractive training of advanced classifiers for mining remote sensing image archivesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage773en_US
dc.citation.epage782en_US
dc.identifier.doi10.1145/1014052.1016913en_US
dc.publisherACMen_US


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