Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61562
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorWang, Qen_US
dc.creatorShi, Wen_US
dc.creatorAtkinson, PMen_US
dc.date.accessioned2016-12-19T08:56:20Z-
dc.date.available2016-12-19T08:56:20Z-
dc.identifier.issn0196-2892en_US
dc.identifier.urihttp://hdl.handle.net/10397/61562-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication Wang, Q., Shi, W., & Atkinson, P. M. (2016). Spatiotemporal subpixel mapping of time-series images. IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5397-5411 is available at https://doi.org/10.1109/TGRS.2016.2562178en_US
dc.subjectLand cover/land use (LCLU) monitoringen_US
dc.subjectSpatiotemporal dependenceen_US
dc.subjectSubpixel mapping (SPM)en_US
dc.subjectSuperresolution mappingen_US
dc.subjectTime-series images (TSIs)en_US
dc.titleSpatiotemporal subpixel mapping of time-series imagesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage5397en_US
dc.identifier.epage5411en_US
dc.identifier.volume54en_US
dc.identifier.issue9en_US
dc.identifier.doi10.1109/TGRS.2016.2562178en_US
dcterms.abstractLand cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on geoscience and remote sensing, Sept. 2016, v. 54, no. 9, p. 5397-5411en_US
dcterms.isPartOfIEEE transactions on geoscience and remote sensingen_US
dcterms.issued2016-09-
dc.identifier.isiWOS:000382689300030-
dc.identifier.scopus2-s2.0-84971449644-
dc.identifier.ros2016003744-
dc.identifier.eissn1558-0644en_US
dc.identifier.rosgroupid2016003675-
dc.description.ros2016-2017 > Academic research: refereed > Publication in refereed journalen_US
dc.description.validate201804_a bcmaen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLSGI-0429-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China;National Administration of Surveying; Ministry of Science and Technology of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6646873-
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