{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:52:45Z","timestamp":1771336365889,"version":"3.50.1"},"reference-count":45,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2019,9,1]]},"DOI":"10.1587\/transinf.2018edp7374","type":"journal-article","created":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T22:11:09Z","timestamp":1567289469000},"page":"1819-1832","source":"Crossref","is-referenced-by-count":1,"title":["Fast Hyperspectral Unmixing via Reweighted Sparse Regression"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Hongwei","family":"HAN","sequence":"first","affiliation":[{"name":"Geomathematics Key Laboratory of Sichuan Province in Chengdu University of Technology"},{"name":"Engineering & Technical College of Chengdu University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"GUO","sequence":"additional","affiliation":[{"name":"Geomathematics Key Laboratory of Sichuan Province in Chengdu University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maozhi","family":"WANG","sequence":"additional","affiliation":[{"name":"Geomathematics Key Laboratory of Sichuan Province in Chengdu University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingbin","family":"ZHANG","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"ZHANG","sequence":"additional","affiliation":[{"name":"College of Computer Science, Neijiang Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] F. Li, M.K. Ng, and R.J. Plemmons, \u201cCoupled segmentation and denoising\/deblurring models for hyperspectral material identification,\u201d Numer. Linear Algebra Appl., vol.19, no.1, pp.153-173, 2012. 10.1002\/nla.750","DOI":"10.1002\/nla.750"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] X.-L. Zhao, F. Wang, T.-Z. Huang, M.K. Ng, and R.J. Plemmons, \u201cDeblurring and Sparse Unmixing for Hyperspectral Images,\u201d IEEE Trans. Geosci. Remote Sens., vol.51, no.7, pp.4045-4058, 2013. 10.1109\/tgrs.2012.2227764","DOI":"10.1109\/TGRS.2012.2227764"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] G. Martin and J.M. Bioucas-Dias, \u201cHyperspectral Blind Reconstruction from Random Spectral Projections,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.9, no.6, pp.2390-2399, 2016. 10.1109\/jstars.2016.2541541","DOI":"10.1109\/JSTARS.2016.2541541"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] W. Tang, Z.W. Shi, Y. Wu, and C.S. Zhang, \u201cSparse unmixing of hyperspectral data using spectral a priori information,\u201d IEEE Trans. Geosci. Remote Sens., vol.53, no.2, pp.770-783, 2016.","DOI":"10.1109\/TGRS.2014.2328336"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] X.S. Liu, W. Xia, B. Wang, and L.M. Zhang, \u201cAn approach based on constrained nonnegative matrix factorization to unmix hyperspectral data,\u201d IEEE Trans. Geosci. Remote Sens., vol.49, no.2, pp.757-772, 2011. 10.1109\/tgrs.2010.2068053","DOI":"10.1109\/TGRS.2010.2068053"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] L.N. Zhuang and J.M. Bioucas-Dias, \u201cFast hyperspectral image denoising and inpainting based on low-rank and sparse representations,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.11, no.3, pp.730-742, 2018.","DOI":"10.1109\/JSTARS.2018.2796570"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] J. Sigurdsson, M.O. Ulfarsson, J.R. Sveinsson, and J.M. Bioucas-Dias, \u201cSparse Distributed Multitemporal Hyperspectral Unmixing,\u201d IEEE Trans. Geosci. Remote Sens., vol.55, no.11, pp.6069-6084, 2017. 10.1109\/tgrs.2017.2720539","DOI":"10.1109\/TGRS.2017.2720539"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Y.-Q. Zhao and J.X. Yang, \u201cHyperspectral image denoising via sparse representation and low-rank constraint,\u201d IEEE Trans. Geosci. Remote Sens., vol.53, no.1, pp.296-308, 2015. 10.1109\/tgrs.2014.2321557","DOI":"10.1109\/TGRS.2014.2321557"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] C.B. Li, T. Sun, K.F. Kelly, and Y. Zhang, \u201cA compressive sensing and unmixing scheme for hyperspectral data processing,\u201d IEEE Trans. Image Process., vol.21, no.3, pp.1200-1210, 2012. 10.1109\/tip.2011.2167626","DOI":"10.1109\/TIP.2011.2167626"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] J.M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, \u201cHyperspectral unmixing overview Geometrical, statistical and sparse regression-based approaches,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.5, no.2, pp.354-379, 2012. 10.1109\/jstars.2012.2194696","DOI":"10.1109\/JSTARS.2012.2194696"},{"key":"11","unstructured":"[11] S.Z. Zhang, J. Li, H.C. Li, C.Z. Deng, and A. Plaza, \u201cSpectral-spatial weighted sparse regression for hyperspectral image unmixing,\u201d IEEE Trans. Geosci. Remote Sens., vol.56, no.6, pp.3265-3276, 2018."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] X.Q. Lu, H. Wu, Y. Yuan, P.K. Yan, and X.L. Li, \u201cMainfold regularized sparse NMF for hyperspectral unmixing,\u201d IEEE Trans. Geosci. Remote Sens., vol.51, no.5, pp.2815-2826, 2013. 10.1109\/tgrs.2012.2213825","DOI":"10.1109\/TGRS.2012.2213825"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] M.-D. Iordache, J.M. Bioucas-Dias, A. Plaza, and B. Somers, \u201cMusic-CSR Hyperspectral unmixing via multiple signal classification and collaborative sparse regression,\u201d IEEE Trans. Geosci. Remote Sens., vol.52, no.7, pp.4364-4382, 2014. 10.1109\/tgrs.2013.2281589","DOI":"10.1109\/TGRS.2013.2281589"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] Y.T. Qian, S. Jia, J. Zhou, and A. Robles-Kelly, \u201cHyperspectral unmixing via L1\/2 sparsity-constrained nonnegative matrix factorization,\u201d IEEE Trans. Geosci. Remote Sens., vol.49, no.11, pp.4282-4297, 2011. 10.1109\/tgrs.2011.2144605","DOI":"10.1109\/TGRS.2011.2144605"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] J.M. Liu, C.X. Zhang, J.S. Zhang, H.R. Li, and Y.L. Gao, \u201cManifold regularization for sparse unmixing of hyperspectral images,\u201d Springerplus, vol.5, no.1, p.2007, 2016. 10.1186\/s40064-016-3671-6","DOI":"10.1186\/s40064-016-3671-6"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] C.Y. Zheng, H. Li, Q. Wang, and C.L.P. Chen, \u201cReweighted Sparse Regression for Hyperspectral Unmixing,\u201d IEEE Trans. Geosci. Remote Sens., vol.54, no.1, pp.479-488, 2015. 10.1109\/tgrs.2015.2459763","DOI":"10.1109\/TGRS.2015.2459763"},{"key":"17","unstructured":"[17] R. Heylen, J. Chen, M. Parente, and P. Gader, \u201cA review of nonlinear hyperspectral unmixing methods,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.7, no.6, pp.1844-1868, 2014."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J.M. Bioucas-Dias, \u201cA variable splitting augmented Lagrangian approach to linear spectral unmixing,\u201d Proc. 1st WHISPERS., pp.1-4, 2009. 10.1109\/whispers.2009.5289072","DOI":"10.1109\/WHISPERS.2009.5289072"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] F.D.V.D. Meer and X.P. Jia, \u201cCollinearity and orthogonality of endmembers in linear spectral unmixing,\u201d Int. J. Appl. Earth Observ. Geoinf., vol.18, no.1, pp.491-503, 2012. 10.1016\/j.jag.2011.10.004","DOI":"10.1016\/j.jag.2011.10.004"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] B. Somers, K. Cools, S. Delalieux, J. Stuckens, D.V.D. Zande, W.W. Verstraeten, and P. Coppin, \u201cNonlinear hyperspectral mixture analysis for tree cover estimates in orchards,\u201d Remote Sens. Environ., vol.113, no.6, pp.1183-1193, 2009. 10.1016\/j.rse.2009.02.003","DOI":"10.1016\/j.rse.2009.02.003"},{"key":"21","unstructured":"[21] A. Halimi, Y. Altmann, N. Dobigeon, J.Y. Tourneret, P. Gader, A.J. Plaza, and C.Y. Chi, \u201cNonlinear unmixing of hyperspectral images using a generalized bilinear model,\u201d IEEE Trans. Geosci. Remote Sens., vol.49, no.11, pp.4153-4162, 2011."},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] X.R. Zhang, C. Li, J.Y. Zhang, Q.M. Chen, J. Feng, L.C. Jiao, and H.Y. Zhou, \u201cHyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning,\u201d Remote Sens., vol.10, no.2, p.339, 2018. 10.3390\/rs10020339","DOI":"10.3390\/rs10020339"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] Y.F. Zhong, R.Y. Feng, and L.P. Zhang, \u201cNon-Local Sparse Unmixing for Hyperspectral remote sensing imagery,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.7, no.6, pp.1889-1909, 2014. 10.1109\/jstars.2013.2280063","DOI":"10.1109\/JSTARS.2013.2280063"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] W.-K. Ma, J.M. Bioucas-Dias, T.-H. Chan, N. Gillis, P. Gader, A.J. Plaza, A. Ambikapathi, and C.-Y. Chi, \u201cA signal processing perspective on hyperspectral unmixing: insights from remote sensing,\u201d IEEE Signal Process. Mag., vol.31, no.1, pp.67-81, 2014. 10.1109\/msp.2013.2279731","DOI":"10.1109\/MSP.2013.2279731"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] R. Wang, H.-C. Li, A. Pizurica, J. Li, A. Plaza, and W.J. Emery, \u201cHyperspectral unmixing using double reweighted sparse regression and total variation,\u201d IEEE Geosci. Remote Sens. Lett., vol.14, no.7, pp.1146-1150, 2017. 10.1109\/lgrs.2017.2700542","DOI":"10.1109\/LGRS.2017.2700542"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] D. Manolakis, C. Siracusa, and G. Shaw, \u201cHyperspectral subpixel target detection using the linear mixing model,\u201d IEEE Trans. Geosci. Remote Sens., vol.39, no.7, pp.1392-1409, 2001. 10.1109\/36.934072","DOI":"10.1109\/36.934072"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] J.M. Bioucas-Dias and M.A.T. Figueiredo, \u201cAlternating direction algorithms for constrained sparse regression application to hyperspectral unmixing,\u201d Proc. 2nd WHISPERS., pp.1-4, 2010. 10.1109\/whispers.2010.5594963","DOI":"10.1109\/WHISPERS.2010.5594963"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] M.-D. Iordache, J.M. Bioucas-Dias, and A. Plaza, \u201cCollaborative sparse regression for hyperspectral unmixing,\u201d IEEE Trans. Geosci. Remote Sens., vol.52, no.1, pp.341-354, 2014. 10.1109\/tgrs.2013.2240001","DOI":"10.1109\/TGRS.2013.2240001"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] M.-D. Iordache, J.M. Bioucas-Dias, and A. Plaza, \u201cHyperspectral unmixing with sparse group lasso,\u201d IEEE Geosci. Remote Sens. Symp., vol.24, no.8, pp.3586-3589, 2011. 10.1109\/igarss.2011.6049999","DOI":"10.1109\/IGARSS.2011.6049999"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] M.-D. Iordache, A. Plaza, and J.M. Bioucas-Dias, \u201cOn the Use of Spectral Libraries to perform Sparse Unmixing of Hyperspectral Data,\u201d Proc. 2nd WHISPERS., pp.1-4, 2010. 10.1109\/whispers.2010.5594888","DOI":"10.1109\/WHISPERS.2010.5594888"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] X. Fu, W.-K. Ma, T.-H. Chan, and J.M. Bioucas-Dias, \u201cSelf-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search Are Related,\u201d IEEE J. Sel. Topics Signal Process., vol.9, no.6, pp.1128-1141, 2015. 10.1109\/jstsp.2015.2410763","DOI":"10.1109\/JSTSP.2015.2410763"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] X. Fu, W.-K. Ma, J.M. Bioucas-Dias, and T.-H. Chan, \u201cSemiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches,\u201d IEEE Trans. Geosci. Remote Sens., vol.54, no.9, pp.5171-5184, 2016. 10.1109\/tgrs.2016.2557340","DOI":"10.1109\/TGRS.2016.2557340"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] A. Zare and P. Gader, \u201cSparsity promoting iterated constrained endmember detection in hyperspectral imagery,\u201d IEEE Geosci. Remote Sens. Lett., vol.4, no.3, pp.446-450, 2007. 10.1109\/lgrs.2007.895727","DOI":"10.1109\/LGRS.2007.895727"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] A. Huck and M. Guillaume, \u201cRobust hyperspectral data unmixing with spatial and spectral regularized NMF,\u201d Proc. 2nd WHISPERS., pp.1-4, 2010. 10.1109\/whispers.2010.5594915","DOI":"10.1109\/WHISPERS.2010.5594915"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] M.-D. Iordache, J.M. Bioucas-Dias, and A. Plaza, \u201cSparse unmixing of hyperspectral data,\u201d IEEE Trans. Geosci. Remote Sens., vol.49, no.6, pp.2014-2039, 2011. 10.1109\/tgrs.2010.2098413","DOI":"10.1109\/TGRS.2010.2098413"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] J.M. Liu, J.S. Zhang, Y.L. Gao, C.X. Zhang, and Z.H. Li, \u201cEnhancing spectral unmixing by local neighborhood weights,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.5, no.5, pp.1545-1552, 2012. 10.1109\/jstars.2012.2199282","DOI":"10.1109\/JSTARS.2012.2199282"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] M.-D. Iordache, J.M. Bioucas-Dias, and A. Plaza, \u201cTotal variation spatial regularization for sparse hyperspectral unmixing,\u201d IEEE Trans. Geosci. Remote Sens., vol.50, no.11, pp.4484-4502, 2012. 10.1109\/tgrs.2012.2191590","DOI":"10.1109\/TGRS.2012.2191590"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] M.-D. Iordache, J.M. Bioucas-Dias, and A. Plaza, \u201cDictionary pruning in sparse unmixing of hyperspectral data,\u201d Proc. 4th WHISPERS., vol.49, no.6, pp.1-4, 2012. 10.1109\/whispers.2012.6874329","DOI":"10.1109\/WHISPERS.2012.6874329"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] A. Erturk, M.-D. Iordache, and A. Plaza, \u201cSparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection,\u201d IEEE J. Sel. Topics Appl. Earth Observ., vol.10, no.1, pp.321-330, 2017. 10.1109\/jstars.2016.2606514","DOI":"10.1109\/JSTARS.2016.2606514"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] J.M. Bioucas-Dias and J.M.P. Nascimento, \u201cHyperspectral Subspace Identification,\u201d IEEE Trans. Geosci. Remote Sens., vol.46, no.8, pp.2435-2445, 2008. 10.1109\/tgrs.2008.918089","DOI":"10.1109\/TGRS.2008.918089"},{"key":"41","unstructured":"[41] F.Y. Zhu, Y. Wang, B. Fan, G.F. Meng, and C.H. Pan, \u201cEffective spectral unmixing via robust representation and learning-based sparsity,\u201d IEEE J. Sel. Topics Signal Process., vol.19, pp.1-13, 2014."},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] A. Halimi, J.M. Bioucas-Dias, N. Dobigeon, G.S. Buller, and S. Mclaughlin, \u201cFast hyperspectral unmixing in presence of nonlinearity or mismodeling effects,\u201d IEEE Trans. Comput. Imaging, vol.3, no.2, pp.146-159, 2017.","DOI":"10.1109\/TCI.2016.2631979"},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] E.J. Cand\u00e8s, M.B. Wakin, and S.P. Boyd, \u201cEnhancing sparsity by reweighted L1 minimization,\u201d J. Fourier Anal. Appl., vol.14, no.5-6, pp.877-905, 2008. 10.1007\/s00041-008-9045-x","DOI":"10.1007\/s00041-008-9045-x"},{"key":"44","unstructured":"[44] F. Yin, \u201cStudy on the theory and method of linear hyperspectral unmixing and its application,\u201d Doctoral dissertation, CDUT, 2016."},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] S. Wang, T.-Z. Huang, X.-L. Zhao, G. Liu, and Y.G. Cheng, \u201cDouble Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing,\u201d Remote Sens., vol.10, no.7, p.1046, 2018. 10.3390\/rs10071046","DOI":"10.3390\/rs10071046"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/9\/E102.D_2018EDP7374\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,7]],"date-time":"2019-09-07T03:27:31Z","timestamp":1567826851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/9\/E102.D_2018EDP7374\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,1]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2018edp7374","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,1]]}}}