{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:12:45Z","timestamp":1760242365081,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,5,31]],"date-time":"2017-05-31T00:00:00Z","timestamp":1496188800000},"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, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR) or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently.<\/jats:p>","DOI":"10.3390\/rs9060541","type":"journal-article","created":{"date-parts":[[2017,5,31]],"date-time":"2017-05-31T10:41:04Z","timestamp":1496227264000},"page":"541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation"],"prefix":"10.3390","volume":"9","author":[{"given":"Elham","family":"Ghasrodashti","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Shiraz University of Technology, 13876-71557 Shiraz, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azam","family":"Karami","sequence":"additional","affiliation":[{"name":"Department of Physics, Shahid Bahonar University of Kerman, 7616914111 Kerman, Iran"},{"name":"Vision Lab, University of Antwerp, 2610 Antwerp, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob","family":"Heylen","sequence":"additional","affiliation":[{"name":"Vision Lab, University of Antwerp, 2610 Antwerp, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Scheunders","sequence":"additional","affiliation":[{"name":"Vision Lab, University of Antwerp, 2610 Antwerp, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TIP.2005.854479","article-title":"Super-resolution reconstruction of hyperspectral images","volume":"14","author":"Akgun","year":"2005","journal-title":"IEEE Trans. Image Process"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/LGRS.2013.2272191","article-title":"Spectral superresolution of hyperspectral imagery using reweighted l1 spatial filtering","volume":"11","author":"Charles","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/JSTARS.2013.2292824","article-title":"Hyperspectral imagery superresolution by spatial-spectral joint nonlocal similarity","volume":"7","author":"Zhao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6574","DOI":"10.1109\/TGRS.2014.2298056","article-title":"Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing","volume":"52","author":"Bendoumi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2011.2161320","article-title":"Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion","volume":"50","author":"Yokoya","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/TGRS.2014.2375320","article-title":"A convex formulation for hyperspectral image superresolution via subspace-based regularization","volume":"53","author":"Simoes","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7236","DOI":"10.1109\/TGRS.2016.2598784","article-title":"Multi-band image fusion based on spectral unmixing","volume":"54","author":"Wei","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/JSTARS.2014.2356512","article-title":"Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image","volume":"8","author":"Sun","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2016.2570433","article-title":"A Computationally Efficient Algorithm for Fusing Multispectral and Hyperspectral Images","volume":"54","author":"Guerra","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bieniarz, J., M\u00fcller, R., Zhu, X.X., and Reinartz, P. (2014, January 13\u201318). Hyperspectral image resolution enhancement based on joint sparsity spectral unmixing. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947017"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TGRS.2012.2213604","article-title":"A sparse image fusion algorithm with application to pansharpening","volume":"51","author":"Zhu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Shafait, F., and Mian, A. (2014). Sparse Spatio-Spectral Representation for Hyperspectral Image Super-Resolution, Springer International Publishing.","DOI":"10.1109\/CVPR.2015.7298986"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TGRS.2013.2253612","article-title":"Spatial and spectral image fusion using sparse matrix factorization","volume":"52","author":"Huang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1109\/TGRS.2014.2381272","article-title":"Hyperspectral and multispectral image fusion based on a sparse representation","volume":"53","author":"Wei","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yokoya, N., Grohnfeldt, C., and Chanussot, J. (2017). Hyperspectral and Multispectral Data Fusion: A Comparative Review. IEEE Geosci. Remote Sens. Mag., 1\u201325.","DOI":"10.1109\/MGRS.2016.2637824"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1109\/JSTARS.2016.2528339","article-title":"Fusion of hyperspectral and multispectral images using spectral unmixing and sparse coding","volume":"9","author":"Nezhad","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Licciardi, G., Veganzones, M.A., Simoes, M., Bioucas-Dias, J.M., and Chanussot, J. (2014, January 25\u201327). Super-resolution of hyperspectral images using local spectral unmixing. Proceedings of the IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2014), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077569"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","article-title":"Hyperspectral pansharpening: A review","volume":"3","author":"Loncan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"591","DOI":"10.14358\/PERS.72.5.591","article-title":"MTFtailored multiscale fusion of high-resolution MS and Pan imagery","volume":"72","author":"Aiazzi","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/LGRS.2013.2281996","article-title":"Contrast and error-based fusion schemes for multispectral image pansharpening","volume":"11","author":"Vivone","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.1109\/JSTARS.2015.2420582","article-title":"Processing of multiresolution thermal hyperspectral and digital color data: Outcome of the 2014 IEEE grss data fusion contest","volume":"8","author":"Liao","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.1109\/TGRS.2011.2155070","article-title":"Fully constrained least-squares spectral unmixing by simplex projection","volume":"49","author":"Heylen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"The k-svd: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2009, January 14\u201318). Online dictionary learning for sparse coding. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553463"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal recovery from random measurements via orthogonal matching pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TIP.2010.2076294","article-title":"An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems","volume":"20","author":"Afonso","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1109\/TIP.2010.2047910","article-title":"Fast image recovery using variable splitting and constrained optimization","volume":"19","author":"Afonso","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TIP.2015.2496263","article-title":"Hyperspectral super-resolution of locally low rank images from complementary multisource data","volume":"25","author":"Veganzones","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TGRS.2004.835294","article-title":"On the possibility of automatic multisensor image registration","volume":"42","author":"Inglada","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2010.10.003","article-title":"Orthorectification of VHR optical satellite data exploiting the geometric accuracy of TerraSAR-X data","volume":"66","author":"Reinartz","year":"2011","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_33","first-page":"633","article-title":"Geometrically constrained multiphoto matching","volume":"54","author":"Gruen","year":"1988","journal-title":"Photogram. Eng. Remote Sens."},{"key":"ref_34","unstructured":"Soukal, P., and Baltsavias, E. (2012, January 26\u201330). Image matching error detection with focus on matching of SAR and optical images. Proceedings of the 33rd Asian Conference on Remote Sensing, Pattaya, Thailand."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5054","DOI":"10.1109\/TGRS.2015.2417098","article-title":"Band-specific shearlet-based hyperspectral image noise reduction","volume":"53","author":"Karami","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Wald, L. (2000, January 26\u201328). Quality of high resolution synthesised images: Is there a simple criterion. Proceedings of the Third Conference \u201cFusion of Earth Data: Merging Point Measurements, Raster Maps and Remotely Sensed Images\u201d, Sophia Antipolis, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"27:1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:37:30Z","timestamp":1760207850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,31]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["rs9060541"],"URL":"https:\/\/doi.org\/10.3390\/rs9060541","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2017,5,31]]}}}