dc.contributor.author | Skjelvareid, Martin Hansen | |
dc.contributor.author | Rinde, Eli | |
dc.contributor.author | Hancke, Kasper | |
dc.contributor.author | Blix, Katalin | |
dc.contributor.author | Hoarau, Galice Guillaume | |
dc.date.accessioned | 2023-11-06T12:17:35Z | |
dc.date.available | 2023-11-06T12:17:35Z | |
dc.date.issued | 2023-10-20 | |
dc.description.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. | en_US |
dc.identifier.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 | en_US |
dc.identifier.cristinID | FRIDAID 2191987 | |
dc.identifier.doi | 10.1109/IGARSS52108.2023.10282809 | |
dc.identifier.issn | 2153-6996 | |
dc.identifier.issn | 2153-7003 | |
dc.identifier.uri | https://hdl.handle.net/10037/31681 | |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.journal | IEEE International Geoscience and Remote Sensing Symposium proceedings | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.title | Mapping Marine Macroalgae along the Norwegian Coast Using Hyperspectral UAV Imaging and Convolutional Nets for Semantic Segmentation | en_US |
dc.type.version | acceptedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |