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) |