Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing

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dc.contributor.authorFernandez-Beltran, Ruben
dc.contributor.authorDemir, Begüm
dc.contributor.authorPla, Filiberto
dc.contributor.authorPlaza, Antonio
dc.date.accessioned2020-11-18T10:20:10Z
dc.date.available2020-11-18T10:20:10Z
dc.date.issued2020-02-06
dc.description.abstractUnsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and computation. Although existing unsupervised hashing methods are suitable for operational applications, they exhibit limitations when accurately modeling the complex semantic content present in RS images using binary codes (in an unsupervised manner). To address this problem, in this letter, we introduce a novel unsupervised hashing method that takes advantage of the generative nature of probabilistic topic models to encapsulate the hidden semantic patterns of the data into the final binary representation. Specifically, we introduce a new probabilistic latent semantic hashing (pLSH) model to effectively learn the hash codes using three main steps: 1) data grouping, where the input RS archive is clustered into several groups; 2) topic computation, where the pLSH model is used to uncover highly descriptive hidden patterns from each group; and 3) hash code generation, where the data probability distributions are thresholded to generate the final binary codes. Our experimental results, obtained on two benchmark archives, reveal that the proposed method significantly outperforms state-of-the-art unsupervised hashing methods.en
dc.description.sponsorshipEC/H2020/734541/EU/Tools for Mapping Human Exposure to Risky Environmental conditions by means of Ground and Earth Observation Data/EOXPOSUREen
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarthen
dc.identifier.eissn1558-0571
dc.identifier.issn1545-598X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/11992
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10872
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otherhash codesen
dc.subject.otherimage retrievalen
dc.subject.otherprobabilistic topic modelsen
dc.subject.otherremote sensingen
dc.subject.otherRSen
dc.subject.otherunsupervised hashingen
dc.titleUnsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashingen
dc.typeArticleen
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1109/LGRS.2020.2969491en
dcterms.bibliographicCitation.journaltitleIEEE Geoscience and Remote Sensing Lettersen
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
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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