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Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Rasti, Behnood
dc.contributor.author Ghamisi, Pedram
dc.contributor.author Ulfarsson, Magnus
dc.date.accessioned 2020-10-13T12:56:42Z
dc.date.available 2020-10-13T12:56:42Z
dc.date.issued 2019-01-10
dc.identifier.citation Rasti, B.; Ghamisi, P.; Ulfarsson, M.O. Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis. Remote Sens. 2019, 11, 121.
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/20.500.11815/2112
dc.description Publisher's version (útgefin grein)
dc.description.abstract In this paper, we develop a hyperspectral feature extraction method called sparse and smooth low-rank analysis (SSLRA). First, we propose a new low-rank model for hyperspectral images (HSIs) where we decompose the HSI into smooth and sparse components. Then, these components are simultaneously estimated using a nonconvex constrained penalized cost function (CPCF). The proposed CPCF exploits total variation penalty, ℓ 1 penalty, and an orthogonality constraint. The total variation penalty is used to promote piecewise smoothness, and, therefore, it extracts spatial (local neighborhood) information. The ℓ 1 penalty encourages sparse and spatial structures. Additionally, we show that this new type of decomposition improves the classification of the HSIs. In the experiments, SSLRA was applied on the Houston (urban) and the Trento (rural) datasets. The extracted features were used as an input into a classifier (either support vector machines (SVM) or random forest (RF)) to produce the final classification map. The results confirm improvement in classification accuracy compared to the state-of-the-art feature extraction approaches.
dc.description.sponsorship This research received no external funding. However, the contribution of Pedram Ghamisi is supported by the High Potential Program offered by Helmholtz-Zentrum Dresden-Rossendorf.
dc.format.extent 121
dc.language.iso en
dc.publisher MDPI AG
dc.relation.ispartofseries Remote Sensing;11(2)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Classification
dc.subject Constrained penalized cost function
dc.subject Feature extraction
dc.subject Hyperspectral image
dc.subject Low-rank
dc.subject Smooth features
dc.subject Sparse features
dc.subject Total variation
dc.subject Myndgreining (upplýsingatækni)
dc.subject Litrófsgreining
dc.title Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis
dc.type info:eu-repo/semantics/article
dcterms.license This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
dc.description.version Peer Reviewed
dc.identifier.journal Remote Sensing
dc.identifier.doi 10.3390/rs11020121
dc.relation.url http://www.mdpi.com/2072-4292/11/2/121/pdf
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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