Automatic detection of compound structures by joint selection of region groups from a hierarchical segmentation

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dc.citation.epage3501en_US
dc.citation.issueNumber6en_US
dc.citation.spage3485en_US
dc.citation.volumeNumber54en_US
dc.contributor.authorAkçay, H. G.en_US
dc.contributor.authorAksoy, S.en_US
dc.date.accessioned2018-04-12T10:42:36Z
dc.date.available2018-04-12T10:42:36Z
dc.date.issued2016en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractA challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.en_US
dc.identifier.doi10.1109/TGRS.2016.2519245en_US
dc.identifier.issn0196-2892
dc.identifier.urihttp://hdl.handle.net/11693/36505
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TGRS.2016.2519245en_US
dc.source.titleIEEE Transactions on Geoscience and Remote Sensingen_US
dc.subjectContext modelingen_US
dc.subjectGibbs samplingen_US
dc.subjectMarkov random field (MRF)en_US
dc.subjectMaximum entropy distributionen_US
dc.subjectObject detectionen_US
dc.subjectSpatial relationshipsen_US
dc.subjectSwendsen-wang samplingen_US
dc.subjectImage reconstructionen_US
dc.subjectMarkov processesen_US
dc.subjectContextual modelingen_US
dc.subjectHeterogeneous compoundsen_US
dc.subjectHierarchical segmentationen_US
dc.subjectMaximum entropy distributionen_US
dc.subjectRemote sensing imagesen_US
dc.subjectVery high spatial resolution imagesen_US
dc.subjectRemote sensingen_US
dc.subjectHierarchical systemen_US
dc.subjectImage analysisen_US
dc.subjectMarkov chainen_US
dc.subjectMaximum entropy analysisen_US
dc.subjectRemote sensingen_US
dc.subjectSamplingen_US
dc.subjectSegmentationen_US
dc.subjectSpatial distributionen_US
dc.subjectUrban areaen_US
dc.titleAutomatic detection of compound structures by joint selection of region groups from a hierarchical segmentationen_US
dc.typeArticleen_US

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