Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data

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dc.authoridMahdianpari, Masoud/0000-0002-7234-959X
dc.authoridJamali, Ali/0000-0002-6073-5493
dc.contributor.authorJamali, Ali
dc.contributor.authorMahdianpari, Masoud
dc.date.accessioned2024-09-29T16:08:15Z
dc.date.available2024-09-29T16:08:15Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing.en_US
dc.identifier.doi10.3390/rs14020359
dc.identifier.issn2072-4292
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85122965038en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/rs14020359
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7424
dc.identifier.volume14en_US
dc.identifier.wosWOS:000758439500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofRemote Sensingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSwin transformeren_US
dc.subject3D convolutional neural networken_US
dc.subjectcoastal wetlandsen_US
dc.subjectNew Brunswicken_US
dc.subjectrandom foresten_US
dc.subjectsupport vector machineen_US
dc.subjectdeep learningen_US
dc.titleSwin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Dataen_US
dc.typeArticleen_US

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