dc.contributor.author | Bore, Fredrik | |
dc.contributor.author | Taraldsen, Andreas | |
dc.date.accessioned | 2018-09-19T06:51:29Z | |
dc.date.available | 2018-09-19T06:51:29Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://hdl.handle.net/11250/2563316 | |
dc.description | Master's thesis Information- and communication technology IKT590 - University of Agder 2018 | nb_NO |
dc.description.abstract | Semantic segmentation of images is of increasing interest in the eld of
computer vision and machine learning. Accurate and e cient segmentation
methods is required for many of todays modern applications. This the-
sis provides a review of deep learning methods for semantic segmentation
of satellite images. Firstly, we compare di erent state-of-the-art methods.
Next, we explore the bene ts of using multiple spectral bands of data as
compared to the traditional RGB bands. Finally, a look at future possibil-
ities with segmentation using capsule networks. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Universitetet i Agder ; University of Agder | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.subject | IKT590 | nb_NO |
dc.title | Deep Convolutional Neural Networks for Semantic Segmentation of Multi-Band Satellite Images | nb_NO |
dc.type | Master thesis | nb_NO |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | nb_NO |
dc.source.pagenumber | 68 p. | nb_NO |