Advanced Local Binary Patterns for Remote Sensing Image Retrieval

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dc.contributor.authorTekeste, Issayas
dc.contributor.authorDemir, Begüm
dc.date.accessioned2019-11-26T09:53:58Z
dc.date.available2019-11-26T09:53:58Z
dc.date.issued2018-11-05
dc.description© 2018 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
dc.description.abstractThe standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time.en
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarthen
dc.identifier.eissn2153-7003
dc.identifier.isbn978-1-5386-7150-4
dc.identifier.isbn978-1-5386-7151-1
dc.identifier.issn2153-6996
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10401
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9351
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otherlocal binary patternen
dc.subject.othercontent based image retrievalen
dc.subject.otherremote sensingen
dc.titleAdvanced Local Binary Patterns for Remote Sensing Image Retrievalen
dc.typeConference Objecten
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1109/IGARSS.2018.8518856en
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.pageend6858en
dcterms.bibliographicCitation.pagestart6855en
dcterms.bibliographicCitation.proceedingstitleIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposiumen
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Groupde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Remote Sensing Image Analysis Groupde
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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