A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model SAM for River Water Segmentation in CloseRange Remote Sensing Imagery

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dc.article.end-page52085en
dc.article.start-page52067en
dc.citation.doi10.1109/ACCESS.2024.3385425en
dc.contributor.authorArmin Moghimien
dc.contributor.authorMario Welzelen
dc.contributor.authorTurgay Celiken
dc.contributor.authorTorsten Schlurmannen
dc.date.accessioned2024-11-25T12:03:56Z
dc.date.available2024-11-25T12:03:56Z
dc.facultyFACULTY OF ENGINEERING & THE BUILT ENVIRONMENTen
dc.identifier.citationWOSen
dc.identifier.issn2169-3536en
dc.identifier.urihttps://hdl.handle.net/10539/42881
dc.journal.titleA Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model SAM for River Water Segmentation in CloseRange Remote Sensing Imageryen
dc.journal.volume12en
dc.titleA Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model SAM for River Water Segmentation in CloseRange Remote Sensing Imageryen
dc.typeJournal Articleen

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