ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraakEnglish 
    • EnglishEnglish
    • norsknorsk
  • Administration/UB
View Item 
  •   Home
  • Fakultet for ingeniørvitenskap og teknologi
  • Institutt for datateknologi og beregningsorienterte ingeniørfag
  • Artikler, rapporter og annet (datateknologi og beregningsorienterte ingeniørfag)
  • View Item
  •   Home
  • Fakultet for ingeniørvitenskap og teknologi
  • Institutt for datateknologi og beregningsorienterte ingeniørfag
  • Artikler, rapporter og annet (datateknologi og beregningsorienterte ingeniørfag)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Mapping Marine Macroalgae along the Norwegian Coast Using Hyperspectral UAV Imaging and Convolutional Nets for Semantic Segmentation

Permanent link
https://hdl.handle.net/10037/31681
DOI
https://doi.org/10.1109/IGARSS52108.2023.10282809
Thumbnail
View/Open
article.pdf (10.21Mb)
Accepted manuscript version (PDF)
Date
2023-10-20
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Skjelvareid, Martin Hansen; Rinde, Eli; Hancke, Kasper; Blix, Katalin; Hoarau, Galice Guillaume
Abstract
Marine macroalgae form underwater "blue forests" with several important functions. Hyperspectral imaging from unmanned aerial vehicles provides a rich set of spectral and spatial data that can be used to map the distribution of such macroalgae. Results from a study using 81 annotated hyper-spectral images from the Norwegian coast are presented. A U-net convolutional network was used for classification, and accuracies for all macroalgae classes were above 90%, indicating the potential of the method as an accurate tool for blue forest monitoring.
Publisher
IEEE
Citation
Skjelvareid, Rinde, Hancke, Blix, Hoarau. Mapping Marine Macroalgae along the Norwegian Coast Using Hyperspectral UAV Imaging and Convolutional Nets for Semantic Segmentation. IEEE International Geoscience and Remote Sensing Symposium proceedings. 2023
Metadata
Show full item record
Collections
  • Artikler, rapporter og annet (datateknologi og beregningsorienterte ingeniørfag) [171]
Copyright 2023 The Author(s)

Browse

Browse all of MuninCommunities & CollectionsAuthor listTitlesBy Issue DateBrowse this CollectionAuthor listTitlesBy Issue Date
Login

Statistics

View Usage Statistics
UiT

Munin is powered by DSpace

UiT The Arctic University of Norway
The University Library
uit.no/ub - munin@ub.uit.no

Accessibility statement (Norwegian only)