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Hyperspectral Unmixing Using a Neural Network Autoencoder

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Palsson, Burkni
dc.contributor.author Sigurdsson, Jakob
dc.contributor.author Sveinsson, Johannes R.
dc.contributor.author Ulfarsson, Magnus
dc.date.accessioned 2018-09-24T11:26:53Z
dc.date.available 2018-09-24T11:26:53Z
dc.date.issued 2018
dc.identifier.citation Palsson, B., Sigurdsson, J., Sveinsson, J. R., & Ulfarsson, M. O. (2018). Hyperspectral Unmixing Using a Neural Network Autoencoder. IEEE Access, 6, 25646-25656. doi:10.1109/ACCESS.2018.2818280
dc.identifier.issn 2169-3536
dc.identifier.uri https://hdl.handle.net/20.500.11815/854
dc.description.abstract In this paper, we present a deep learning based method for blind hyperspectral unmixing in the form of a neural network autoencoder. We show that the linear mixture model implicitly puts certain architectural constraints on the network, and it effectively performs blind hyperspectral unmixing. Several different architectural configurations of both shallow and deep encoders are evaluated. Also, deep encoders are tested using different activation functions. Furthermore, we investigate the performance of the method using three different objective functions. The proposed method is compared to other benchmark methods using real data and previously established ground truths of several common data sets. Experiments show that the proposed method compares favorably to other commonly used hyperspectral unmixing methods and exhibits robustness to noise. This is especially true when using spectral angle distance as the network's objective function. Finally, results indicate that a deeper and a more sophisticated encoder does not necessarily give better results.
dc.description.sponsorship This work was supported in part by the Icelandic Research Fund under Grant 174075-05 and in part by the Postdoctoral Research Fund at the University of Iceland.
dc.format.extent 25646-25656
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseries IEEE Access;6
dc.rights info:eu-repo/semantics/openAccess
dc.subject Hyperspectral unmixing
dc.subject Autoencoder
dc.subject Deep learning
dc.subject Neural network
dc.subject Spectral angle distance
dc.subject Endmember extraction
dc.subject Hugbúnaðarverkfræði
dc.title Hyperspectral Unmixing Using a Neural Network Autoencoder
dc.type info:eu-repo/semantics/article
dc.description.version Peer Reviewed
dc.identifier.journal IEEE Access
dc.identifier.doi 10.1109/ACCESS.2018.2818280
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)


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