{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:31:51Z","timestamp":1770834711684,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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,     \u2113 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     \u2113 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.<\/jats:p>","DOI":"10.3390\/rs11020121","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1091-9841","authenticated-orcid":false,"given":"Behnood","family":"Rasti","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Iceland, Skolabraut 3, 220 Hafnarfjordur, Iceland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, D-09599 Freiberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0461-040X","authenticated-orcid":false,"given":"Magnus O.","family":"Ulfarsson","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 6, 107 Reykjavik, Iceland"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geos. Remote Sens. Mag."},{"key":"ref_2","unstructured":"Landgrebe, D. (2005). Signal Theory Methods in Multispectral Remote Sensing, Wiley."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LGRS.2014.2337320","article-title":"Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization","volume":"12","author":"Ghamisi","year":"2015","journal-title":"IEEE Geos. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/JPROC.2012.2229082","article-title":"Feature Mining for Hyperspectral Image Classification","volume":"101","author":"Jia","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_5","unstructured":"Benediktsson, J.A., and Ghamisi, P. (2015). Spectral-Spatial Classification of Hyperspectral Remote Sensing Images, Artech House Publishers."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition, Elsevier Science. Computer Science and Scientific Computing.","DOI":"10.1016\/B978-0-08-047865-4.50007-7"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/34.206958","article-title":"Feature extraction based on decision boundaries","volume":"15","author":"Lee","year":"1993","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/TGRS.2004.825578","article-title":"Nonparametric weighted feature extraction for classification","volume":"42","author":"Kuo","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0031-3203(99)00215-0","article-title":"A linear constrained distance-based discriminant analysis for hyperspectral image classification","volume":"34","author":"Du","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/LGRS.2007.900751","article-title":"Modified Fisher\u2019s Linear Discriminant Analysis for Hyperspectral Imagery","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TGRS.2012.2197860","article-title":"Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral-Spatial Feature Extraction","volume":"51","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TGRS.2011.2165957","article-title":"Locality-preserving dimensionality reduction and classification for hyperspectral image analysis","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"1027","article-title":"Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis","volume":"8","author":"Sugiyama","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TGRS.2014.2333539","article-title":"Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification","volume":"53","author":"Zhou","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6114","DOI":"10.1109\/TGRS.2015.2432059","article-title":"Simultaneous sparse graph embedding for hyperspectral image classification","volume":"53","author":"Xue","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","unstructured":"Jolliffe, I. (2002). Principal Component Analysis, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/36.54356","article-title":"Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform","volume":"28","author":"Lee","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hyv\u00e4rinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Analysis, Wiley.","DOI":"10.1002\/0471221317"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4865","DOI":"10.1109\/TGRS.2011.2153861","article-title":"Hyperspectral Image Classification With Independent Component Discriminant Analysis","volume":"49","author":"Villa","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Lee, D.D., and Seung, H.S. (2000). Algorithms for Non-Negative Matrix Factorization, MIT Press. NIPS."},{"key":"ref_22","unstructured":"Lin, B., Tao, G., and Kai, D. (2013, January 19\u201321). Using non-negative matrix factorization with projected gradient for hyperspectral images feature extraction. Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), Melbourne, Australia."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sigurdsson, J., Ulfarsson, M., and Sveinsson, J. (2015, January 26\u201331). Total variation and lq based hyperspectral unmixing for feature extraction and classification. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325794"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6793","DOI":"10.1109\/TGRS.2014.2303155","article-title":"Hyperspectral unmixing with lq regularization","volume":"52","author":"Sigurdsson","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"4099","article-title":"Local Manifold Learning-Based k-Nearest-Neighbor for Hyperspectral Image Classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1109\/LGRS.2014.2306689","article-title":"Dimensionality Reduction of Hyperspectral Images Based on Robust Spatial Information Using Locally Linear Embedding","volume":"11","author":"Fang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","unstructured":"He, X., Cai, D., Yan, S., and Zhang, H.J. (2005, January 17\u201321). Neighborhood preserving embedding. Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing, China."},{"key":"ref_28","unstructured":"Thrun, S., Saul, L., and Scholkopf, B. (2003). Locality Preserving Projections. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1016\/j.neucom.2006.11.007","article-title":"Linear local tangent space alignment and application to face recognition","volume":"70","author":"Zhang","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_30","unstructured":"Fong, M. (2007). Dimension Reduction on Hyperspectral Images, University of California. Technical Report."},{"key":"ref_31","first-page":"4034","article-title":"Double Nearest Proportion Feature Extraction for Hyperspectral-Image Classification","volume":"48","author":"Huang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2017.2786223","article-title":"Modified Tensor Locality Preserving Projection for Dimensionality Reduction of Hyperspectral Images","volume":"15","author":"Deng","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","article-title":"Graph Embedding and Extensions: A General Framework for Dimensionality Reduction","volume":"29","author":"Yan","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3872","DOI":"10.1109\/TGRS.2013.2277251","article-title":"Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery","volume":"52","author":"Ly","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1109\/TGRS.2017.2720584","article-title":"Discriminant Analysis of Hyperspectral Imagery Using Fast Kernel Sparse and Low-Rank Graph","volume":"55","author":"Pan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1145\/2010324.1964964","article-title":"Domain Transform for Edge-aware Image and Video Processing","volume":"30","author":"Gastal","year":"2011","journal-title":"ACM Trans. Graph."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3742","DOI":"10.1109\/TGRS.2013.2275613","article-title":"Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2017.2686842","article-title":"A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification","volume":"55","author":"Sun","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rasti, B., Sveinsson, J.R., and Ulfarsson, M.O. (2014, January 13\u201318). Total Variation Based Hyperspectral Feature Extraction. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947528"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6688","DOI":"10.1109\/TGRS.2014.2301415","article-title":"Wavelet-Based Sparse Reduced-Rank Regression for Hyperspectral Image Restoration","volume":"52","author":"Rasti","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"Rasti, B. (2014). Sparse Hyperspectral Image Modeling and Restoration. [Ph.D. Thesis, Department of Electrical and Computer Engineering, University of Iceland]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6976","DOI":"10.1109\/TGRS.2016.2593463","article-title":"Hyperspectral Feature Extraction Using Total Variation Component Analysis","volume":"54","author":"Rasti","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/LGRS.2015.2485999","article-title":"Hyperspectral Subspace Identification Using SURE","volume":"12","author":"Rasti","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TGRS.2008.918089","article-title":"Hyperspectral Subspace Identification","volume":"46","author":"Nascimento","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","unstructured":"Bertsekas, D. (1995). Nonlinear Programming, Athena Scientific."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Luenberger, D. (2008). Linear Nonlinear Programming, Springer. [3rd ed.].","DOI":"10.1007\/978-0-387-74503-9"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1023\/A:1017501703105","article-title":"Convergence of a block coordinate descent method for nondifferentiable minimization","volume":"109","author":"Tseng","year":"2001","journal-title":"J. Opt. Theory Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1137\/080725891","article-title":"The Split Bregman Method for \u21131-Regularized Problems","volume":"2","author":"Goldstein","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/BF01581204","article-title":"On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators","volume":"55","author":"Eckstein","year":"1992","journal-title":"Math. Program."},{"key":"ref_51","first-page":"2006","article-title":"Sparse Principal Component Analysis","volume":"15","author":"Zou","year":"2004","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_52","unstructured":"He, X.F., and Niyogi, P. (2004). Locality Preserving Projections, MIT Press."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10994-009-5125-7","article-title":"Semi-supervised local Fisher discriminant analysis for dimensionality reduction","volume":"78","author":"Sugiyama","year":"2010","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TGRS.2012.2200106","article-title":"Semi-Supervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images","volume":"51","author":"Liao","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1109\/JSTARS.2016.2522564","article-title":"Feature Extraction of Hyperspectral Images with Semi-Supervised Graph Learning","volume":"9","author":"Luo","year":"2016","journal-title":"IEEE J. Sel. Top. App. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:24:58Z","timestamp":1760185498000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,10]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020121"],"URL":"https:\/\/doi.org\/10.3390\/rs11020121","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,10]]}}}