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dc.contributor.authorAksoy, S.en_US
dc.contributor.authorCinbiş, R. G.en_US
dc.date.accessioned2015-07-28T11:58:32Z
dc.date.available2015-07-28T11:58:32Z
dc.date.issued2010-01en_US
dc.identifier.issn1545-598X
dc.identifier.urihttp://hdl.handle.net/11693/11710
dc.description.abstractSpatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial relationships among objects and using this information for contextual classification and retrieval. The proposed model first identifies image areas that have a high degree of satisfaction of a spatial relation with respect to several reference objects. Then, this information is incorporated into the Bayesian decision rule as spatial priors for contextual classification. The model also supports dynamic queries by using directional relationships as spatial constraints to enable object detection based on the properties of individual objects as well as their spatial relationships to other objects. Comparative experiments using high-resolution satellite imagery illustrate the flexibility and effectiveness of the proposed framework in image mining with significant improvements in both classification and retrieval performance.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Geoscience and Remote Sensing Lettersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/LGRS.2009.2014083en_US
dc.subjectImage classificationen_US
dc.subjectImage retrievalen_US
dc.subjectMathematical morphologyen_US
dc.subjectObject detectionen_US
dc.subjectSpatial relationshipsen_US
dc.titleImage mining using directional spatial constraintsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage33en_US
dc.citation.epage37en_US
dc.citation.volumeNumber7en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1109/LGRS.2009.2014083en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US


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