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dc.contributor.authorArı, C.en_US
dc.contributor.authorAksoy, S.en_US
dc.date.accessioned2016-02-08T11:02:29Z
dc.date.available2016-02-08T11:02:29Z
dc.date.issued2014en_US
dc.identifier.issn0196-2892
dc.identifier.urihttp://hdl.handle.net/11693/26622
dc.description.abstractIncreasing spectral and spatial resolution of new-generation remotely sensed images necessitate the joint use of both types of information for detection and classification tasks. This paper describes a new approach for detecting heterogeneous compound structures such as different types of residential, agricultural, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses Gaussian mixture models (GMMs), in which the individual Gaussian components model the spectral and shape characteristics of the individual primitives and an associated layout model is used to model their spatial arrangements. We propose a novel expectation-maximization (EM) algorithm that solves the detection problem using constrained optimization. The input is an example structure of interest that is used to estimate a reference GMM and construct spectral and spatial constraints. Then, the EM algorithm fits a new GMM to the target image data so that the pixels with high likelihoods of being similar to the Gaussian object models while satisfying the spatial layout constraints are identified without any requirement for region segmentation. Experiments using WorldView-2 images show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques. © 1980-2012 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Geoscience and Remote Sensingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TGRS.2014.2299540en_US
dc.subjectConstrained optimizationen_US
dc.subjectContext modelingen_US
dc.subjectExpectation-maximization (EM)en_US
dc.subjectGaussian mixture model (GMM)en_US
dc.subjectObject detectionen_US
dc.subjectSpectral-spatial classificationen_US
dc.subjectAlgorithmsen_US
dc.subjectHeterogeneityen_US
dc.subjectNumerical modelen_US
dc.subjectPixelen_US
dc.subjectSpatial resolutionen_US
dc.titleDetection of compound structures using a gaussian mixture model with spectral and spatial constraintsen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage6627en_US
dc.citation.epage6638en_US
dc.citation.volumeNumber52en_US
dc.citation.issueNumber10en_US
dc.identifier.doi10.1109/TGRS.2014.2299540en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US


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