{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:52:00Z","timestamp":1762955520981,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,16]],"date-time":"2016-03-16T00:00:00Z","timestamp":1458086400000},"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>Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations.<\/jats:p>","DOI":"10.3390\/rs8030250","type":"journal-article","created":{"date-parts":[[2016,3,16]],"date-time":"2016-03-16T11:26:45Z","timestamp":1458127605000},"page":"250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5678-1369","authenticated-orcid":false,"given":"Ruyi","family":"Feng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5850","authenticated-orcid":false,"given":"Yanfei","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Yunyun","family":"Wu","sequence":"additional","affiliation":[{"name":"The Second Surveying and Mapping of Zhejiang Province, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7998-1045","authenticated-orcid":false,"given":"Da","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3510-4160","authenticated-orcid":false,"given":"Xiong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Liangpei","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.3390\/rs4092619","article-title":"Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers","volume":"4","author":"Johnson","year":"2012","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15361","DOI":"10.3390\/rs71115361","article-title":"Spectral unmixing of forest crown components at close range, airborne and simulated Sentinel-2 and EnMAP spectral image scale","volume":"7","author":"Clasen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/MSP.2013.2279731","article-title":"A signal processing perspective on hyperspectral unmixing: Insights from remote sensing","volume":"1","author":"Ma","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16363","DOI":"10.3390\/rs71215834","article-title":"An endmember extraction method based on artificial bee colony algorithms for hyperspectral remote sensing images","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14000","DOI":"10.3390\/rs71014000","article-title":"A spectral unmixing model for the integration of multi-sensor imagery: A tool to generate consistent time series data","volume":"7","author":"Doxani","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15114","DOI":"10.3390\/rs71115114","article-title":"Increasing the accuracy of mapping urban forest carbon density by combining spatial modeling and spectral unmixing analysis","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.rse.2006.04.020","article-title":"Super-resolution land cover mapping with indicator geostatistics","volume":"104","author":"Boucher","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1109\/LGRS.2015.2441135","article-title":"Burned-area mapping at the subpixel scale with MODIS images","volume":"12","author":"Ling","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","first-page":"166","article-title":"Mapping sub-pixel boundaries from remotely sensed images","volume":"4","author":"Atkinson","year":"1997","journal-title":"Innovat. GIS"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/TGRS.2014.2346535","article-title":"Fast subpixel mapping algorithms for subpixel resolution change detection","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TGRS.2014.2340734","article-title":"An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery","volume":"53","author":"Zhong","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/JSTSP.2015.2416683","article-title":"Subpixel mapping based on conditional random fields for hyperspectral remote sensing imagery","volume":"9","author":"Zhao","year":"2015","journal-title":"IEEE J. Sel. Topics Signal Process."},{"doi-asserted-by":"crossref","unstructured":"Feng, R., Zhong, Y., Xu, X., and Zhang, L. (2015). Adaptive sparse subpixel mapping with a total variation model for remote sensing imagery. IEEE Trans. Geosci. Remote Sens.","key":"ref_14","DOI":"10.1109\/TGRS.2015.2506612"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4424","DOI":"10.1109\/TGRS.2013.2281992","article-title":"Superresolution land-cover mapping using spatial regularization","volume":"52","author":"Ling","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/LGRS.2010.2055034","article-title":"Land cover change mapping at the subpixel scale with different spatial-resolution remotely sensed imagery","volume":"8","author":"Ling","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1080\/01431160500396741","article-title":"Localized soft classification for super-resolution mapping of the shoreline","volume":"27","author":"Muslim","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1016\/j.ecolind.2010.12.016","article-title":"Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices","volume":"11","author":"Li","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_19","first-page":"106","article-title":"Downscaling in remote sensing","volume":"22","author":"Atkinson","year":"2013","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/S0034-4257(01)00242-5","article-title":"Land cover mapping at sub-pixel scales using linear optimization techniques","volume":"79","author":"Verhoeye","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3293","DOI":"10.1080\/01431160500497127","article-title":"A sub-pixel mapping algorithm based on sub-pixel\/pixel spatial attraction models","volume":"27","author":"Mertens","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"839","DOI":"10.14358\/PERS.71.7.839","article-title":"Sub-pixel target mapping from soft-classified, remotely sensed imagery","volume":"71","author":"Atkinson","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1016\/j.neucom.2007.08.033","article-title":"A new sub-pixel mapping algorithm based on a BP neural network with an observation model","volume":"71","author":"Zhang","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1109\/JSTARS.2012.2191537","article-title":"Combining Hopfield neural network and contouring methods to enhance super-resolution mapping","volume":"5","author":"Su","year":"2012","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2005.02.006","article-title":"Super-resolution land cover mapping using a Markov random field based approach","volume":"96","author":"Kasetkasem","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/TGRS.2008.2010863","article-title":"Development and testing of a subpixel mapping algorithm","volume":"47","author":"Ge","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4241","DOI":"10.1080\/01431160310001595073","article-title":"Using genetic algorithms in sub-pixel mapping","volume":"24","author":"Mertens","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/TSMCB.2012.2189561","article-title":"Remote sensing image subpixel mapping based on adaptive differential evolution","volume":"42","author":"Zhong","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. B: Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1080\/01431161.2012.690541","article-title":"Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery","volume":"33","author":"Wang","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2902","DOI":"10.1016\/j.patcog.2013.04.009","article-title":"Sub-pixel mapping based on artificial immune systems for remote sensing imagery","volume":"46","author":"Zhong","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/TGRS.2013.2244095","article-title":"Adaptive subpixel mapping based on a multiagent system for remote-sensing imagery","volume":"52","author":"Xu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/JSTARS.2012.2227246","article-title":"Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery","volume":"6","author":"Xu","year":"2013","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2014.06.019","article-title":"Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery","volume":"96","author":"Zhong","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1137\/090746379","article-title":"Bregmanized nonlocal regularization for deconvolution and sparse reconstruction","volume":"3","author":"Zhang","year":"2010","journal-title":"SIAM J. Imag. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1137\/040616024","article-title":"A review of image denoising algorithms, with a new one","volume":"4","author":"Buades","year":"2005","journal-title":"Multiscale Model. Sim. (SIAM Interdiscip. J.)"},{"unstructured":"Buades, A., Coll, B., and Morel, J.-M. (2005, January 20\u201326). A non-local algorithm for image denoising. Proceedings of the IEEE Computer Society Conf. Computer Vision and Pattern Recognition, San Diego, CA, USA.","key":"ref_36"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/JSTARS.2012.2232904","article-title":"Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation","volume":"6","author":"Qian","year":"2013","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TIP.2008.2008067","article-title":"Generalizing the nonlocal-means to super-resolution reconstruction","volume":"18","author":"Protter","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1137\/060669358","article-title":"Nonlocal linear image regularization and supervised segmentation","volume":"6","author":"Gilboa","year":"2007","journal-title":"Multiscale Model. Sim. (SIAM Interdiscip. J.)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2013.2274875","article-title":"Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/JSTARS.2013.2280063","article-title":"Non-Local sparse unmixing for hyperspectral remote sensing imagery","volume":"7","author":"Zhong","year":"2014","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1137\/070698592","article-title":"Nonlocal operators with applications to image processing","volume":"7","author":"Gilboa","year":"2008","journal-title":"Multiscale Model. Sim."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1137\/040605412","article-title":"An iterative regularization method for total variation-based image restoration","volume":"4","author":"Osher","year":"2005","journal-title":"SIAM Multiscale Model. Sim."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1137\/080725891","article-title":"The split Bregman method for L1-regularized problems","volume":"2","author":"Goldstein","year":"2009","journal-title":"SIAM J. Imag. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/JSTSP.2010.2096798","article-title":"Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution","volume":"5","author":"Villa","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"unstructured":"American ITT Visual Information Solutions Company ENVI Online Tutorials. Available online: http:\/\/www.exelisvis.com\/Learn\/Resources\/Tutorials.aspx.","key":"ref_47"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Sisodia, P.S., Tiwari, V., and Kumar, A. (2014, January 24\u201327). A comparative analysis of remote sensing image classification techniques. Proceedings of the 2014 International Conference on Advanced in Computing, Communications and Informatics, New Delhi, India.","key":"ref_49","DOI":"10.1109\/ICACCI.2014.6968245"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1109\/TGRS.2010.2098413","article-title":"Sparse unmixing of hyperspectral data","volume":"49","author":"Iordache","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:20:50Z","timestamp":1760210450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,16]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,3]]}},"alternative-id":["rs8030250"],"URL":"https:\/\/doi.org\/10.3390\/rs8030250","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,3,16]]}}}