A Novel Multi-Attention Driven System for Multi-Label Remote Sensing Image Classification

dc.contributor.authorSumbul, Gencer
dc.contributor.authorDemir, Begüm
dc.date.accessioned2019-11-25T20:39:50Z
dc.date.available2019-11-25T20:39:50Z
dc.date.issued2019-11-14
dc.description© 2019 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.
dc.description.abstractThis paper presents a novel multi-attention driven system that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image classification. The proposed system consists of four main modules. The first module aims to extract preliminary local descriptors of RS image bands that can be associated to different spatial resolutions. To this end, we introduce a K-Branch CNN, in which each branch extracts descriptors of image bands that have the same spatial resolution. The second module aims to model spatial relationship among local descriptors. This is achieved by a bidirectional RNN architecture, in which Long Short-Term Memory nodes enrich local descriptors by considering spatial relationships of local areas (image patches). The third module aims to define multiple attention scores for local descriptors. This is achieved by a novel patch-based multi-attention mechanism that takes into account the joint occurrence of multiple land-cover classes and provides the attention-based local descriptors. The last module exploits these descriptors for multi-label RS image classification. Experimental results obtained on the BigEarth-Net that is a large-scale Sentinel-2 benchmark archive show the effectiveness of the proposed method compared to a state of the art method.en
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarthen
dc.identifier.eissn2153-7003
dc.identifier.isbn978-1-5386-9154-0
dc.identifier.isbn978-1-5386-9155-7
dc.identifier.issn2153-6996
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10387
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9347
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.othermulti-label image classificationen
dc.subject.otherdeep neural networken
dc.subject.otherattention mechanismen
dc.subject.otherremote sensingen
dc.titleA Novel Multi-Attention Driven System for Multi-Label Remote Sensing Image Classificationen
dc.typeConference Objecten
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1109/IGARSS.2019.8898188en
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.pageend5729en
dcterms.bibliographicCitation.pagestart5726en
dcterms.bibliographicCitation.proceedingstitleIGARSS 2019 - 2019 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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