BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval

dc.contributor.authorSumbul, Gencer
dc.contributor.authorde Wall, Arne
dc.contributor.authorKreuziger, Tristan
dc.contributor.authorMarcelino, Filipe
dc.contributor.authorCosta, Hugo
dc.contributor.authorBenevides, Pedro
dc.contributor.authorCaetane, Mário
dc.contributor.authorDemir, Begüm
dc.contributor.authorMarkl, Volker
dc.date.accessioned2022-01-20T07:22:11Z
dc.date.available2022-01-20T07:22:11Z
dc.date.issued2021-09-29
dc.description.abstractThis article presents the multimodal BigEarthNet (BigEarthNet-MM) benchmark archive consisting of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support deep learning (DL) studies in multimodal, multilabel remote sensing (RS) image retrieval and classification. Each pair of patches in BigEarthNet-MM is annotated with multilabels provided by the CORINE Land Cover (CLC) map of 2018 based on its thematically most detailed level-3 class nomenclature. Our initial research demonstrates that some CLC classes are challenging to accurately describe by considering only (single-date) BigEarthNet-MM images. In this article, we also introduce an alternative class nomenclature as an evolution of the original CLC labels to address this problem. This is achieved by interpreting and arranging the CLC level-3 nomenclature based on the properties of BigEarthNet-MM images in a new nomenclature of 19 classes. In our experiments, we show the potential of BigEarthNet-MM for multimodal, multilabel image retrieval and classification problems by considering several state-of-the-art DL models.en
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarthen
dc.description.sponsorshipBMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Dataen
dc.identifier.eissn2168-6831
dc.identifier.issn2473-2397
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/16171
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-14945
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.othermulti-modal learningen
dc.subject.othermulti-label image retrievalen
dc.subject.otherimage classificationen
dc.subject.otherdeep learningen
dc.subject.otherremote sensingen
dc.subject.otherimage retrievalen
dc.subject.otherbenchmark testingen
dc.titleBigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrievalen
dc.typeArticleen
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1109/MGRS.2021.3089174en
dcterms.bibliographicCitation.issue3en
dcterms.bibliographicCitation.journaltitleIEEE Geoscience and Remote Sensing Magazineen
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.pageend180en
dcterms.bibliographicCitation.pagestart174en
dcterms.bibliographicCitation.volume9en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Technische Informatik und Mikroelektronik>FG Remote Sensing Image Analysis Groupde
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Softwaretechnik und Theoretische Informatik>FG Datenbanksysteme und Informationsmanagement (DIMA)de
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Remote Sensing Image Analysis Groupde
tub.affiliation.groupFG Datenbanksysteme und Informationsmanagement (DIMA)de
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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