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BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval [Software and Data Sets]

2021-09-01Artigo científico Acesso aberto
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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.authorCaetano, Mario
dc.contributor.authorDemir, Begum
dc.contributor.authorMarkl, Volker
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2021-11-29T23:39:47Z
dc.date.available2021-11-29T23:39:47Z
dc.date.issued2021-09-01
dc.descriptionSumbul, G., De Wall, A., Kreuziger, T., Marcelino, F., Costa, H., Benevides, P., Caetano, M., Demir, B., & Markl, V. (2021). BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine, 9(3), 174-180. https://doi.org/10.1109/MGRS.2021.3089174
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.versionauthorsversion
dc.description.versionpublished
dc.format.extent7
dc.format.extent505075
dc.identifier.doi10.1109/MGRS.2021.3089174
dc.identifier.issn2473-2397
dc.identifier.otherPURE: 35040153
dc.identifier.otherPURE UUID: ebe5c1fe-3d3a-4323-af6c-6299598cbdca
dc.identifier.othercrossref: 10.1109/MGRS.2021.3089174
dc.identifier.otherScopus: 85117337468
dc.identifier.otherWOS: 000701246700020
dc.identifier.urihttp://hdl.handle.net/10362/128461
dc.identifier.urlhttps://www.scopus.com/pages/publications/85117337468
dc.identifier.urlhttp://bigearth.net/
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000701246700020
dc.identifier.urlhttps://ieeexplore.ieee.org/document/9552024/
dc.language.isoeng
dc.peerreviewedyes
dc.titleBigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval [Software and Data Sets]en
dc.typejournal article
degois.publication.firstPage174
degois.publication.issue3
degois.publication.lastPage180
degois.publication.titleIEEE Geoscience and Remote Sensing Magazine
degois.publication.volume9
dspace.entity.typePublication
rcaap.rightsopenAccess

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