WETLAND CLASSIFICATION WITH SWIN TRANSFORMER USING SENTINEL-1 AND SENTINEL-2 DATA

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dc.authoridJamali, Ali/0000-0002-6073-5493
dc.contributor.authorJamali, Ali
dc.contributor.authorMohammadimanesh, Fariba
dc.contributor.authorMahdianpari, Masoud
dc.date.accessioned2024-09-29T16:04:28Z
dc.date.available2024-09-29T16:04:28Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.descriptionIEEE International Geoscience and Remote Sensing Symposium (IGARSS) -- JUL 17-22, 2022 -- Kuala Lumpur, MALAYSIAen_US
dc.description.abstractConvolutional Neural Networks (CNNs) have shown promising results in classifying complex remote sensing scenery, particularly in the classification of wetlands. State-of-the-art Natural Language Processing ( NLP) algorithms, on the other hand, are transformers. In this paper, we illustrate the effectiveness of the cutting-edge Swin Transformer for the classification of complex wetlands in New Brunswick, Canada. The precision of the proposed transformer is 0.66, 0.71, 0.75, 0.78, 0.82, 0.83, 0.84, 0.90, 0.90, 0.95, and 0.98 for the recognition of shrub, fen, forested wetland, crop, bog, freshwater marsh, coastal marsh, aquatic bed, grass, urban, and water, respectively. Based on the results, with a relatively high level of overall accuracy of slightly less than 80%, the proposed Swin Transformer is highly capable of complex wetland classification.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.doi10.1109/IGARSS46834.2022.9884602
dc.identifier.endpage6216en_US
dc.identifier.isbn978-1-6654-2792-0
dc.identifier.issn2153-6996
dc.identifier.scopus2-s2.0-85140378337en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage6213en_US
dc.identifier.urihttps://doi.org/10.1109/IGARSS46834.2022.9884602
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6141
dc.identifier.wosWOS:000920916606051en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2022 Ieee International Geoscience and Remote Sensing Symposium (Igarss 2022)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWetland mappingen_US
dc.subjectCNNen_US
dc.subjectTransformeren_US
dc.subjectSwin Transformeren_US
dc.subjectSentinel imageryen_US
dc.titleWETLAND CLASSIFICATION WITH SWIN TRANSFORMER USING SENTINEL-1 AND SENTINEL-2 DATAen_US
dc.typeConference Objecten_US

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