{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:15:17Z","timestamp":1769638517055,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2014,4,28]],"date-time":"2014-04-28T00:00:00Z","timestamp":1398643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, two mixture models are proposed for modeling heterogeneous regions in single-look and multi-look polarimetric SAR images, along with their corresponding maximum likelihood classifiers for land cover classification. The classical Gaussian and Wishart models are suitable for modeling scattering vectors and covariance matrices from homogeneous regions, while their performance deteriorates for regions that are heterogeneous. By comparison, the proposed mixture models reduce the modeling error by expressing the data distribution as a weighted sum of multiple component distributions. For single-look and multi-look polarimetric SAR data, complex Gaussian and complex Wishart components are adopted, respectively. Model parameters are determined by employing the expectation-maximization (EM) algorithm. Two maximum likelihood classifiers are then constructed based on the proposed mixture models. These classifiers are assessed using polarimetric SAR images from the RADARSAT-2 sensor of the Canadian Space Agency (CSA), the AIRSAR sensor of the Jet Propulsion Laboratory (JPL) and the EMISAR sensor of the Technical University of Denmark (DTU). Experiment results demonstrate that the new models fit heterogeneous regions preferably to the classical models and are especially appropriate for extremely heterogeneous regions, such as urban areas. The overall accuracy of land cover classification is also improved due to the more refined modeling.<\/jats:p>","DOI":"10.3390\/rs6053770","type":"journal-article","created":{"date-parts":[[2014,4,28]],"date-time":"2014-04-28T11:40:09Z","timestamp":1398685209000},"page":"3770-3790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Land Cover Classification for Polarimetric SAR Images Based on Mixture Models"],"prefix":"10.3390","volume":"6","author":[{"given":"Wei","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,4,28]]},"reference":[{"key":"ref_1","first-page":"171","article-title":"Identification of terrain cover using the optimum polarimetric classifier","volume":"2","author":"Kong","year":"1987","journal-title":"J. Electromagn. Waves Appl"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7049","DOI":"10.1029\/JB094iB06p07049","article-title":"Classification of earth terrain using polarimetric synthetic aperture radar images","volume":"94","author":"Lim","year":"1989","journal-title":"J. Geophys. Res"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1080\/01431169408954244","article-title":"Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution","volume":"15","author":"Lee","year":"1994","journal-title":"Int. J. Remote Sens"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1163\/156939389X00412","article-title":"K-distribution and polarimetric terrain radar clutter","volume":"3","author":"Yueh","year":"1989","journal-title":"J. Electromagn. Waves Appl"},{"key":"ref_5","unstructured":"Lee, J.S., Schuler, D.L., Lang, R.H., and Ranson, K.J. (1994, January 8\u201312). K-Distribution for Multi-Look Processed Polarimetric SAR Imagery. Pasadena, PA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/36.581981","article-title":"A model for extremely heterogeneous clutter","volume":"35","author":"Frery","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1002\/env.658","article-title":"The polarimetric G distribution for SAR data analysis","volume":"16","author":"Freitas","year":"2005","journal-title":"Environmetrics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1109\/TGRS.2008.923025","article-title":"Classification with a non-Gaussian model for PolSAR data","volume":"46","author":"Doulgeris","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/36.905240","article-title":"Segmentation and classification of vegetated areas using polarimetric SAR image data","volume":"39","author":"Dong","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/LGRS.2008.2002263","article-title":"Region-based classification of polarimetric SAR images using Wishart MRF","volume":"5","author":"Wu","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"513","DOI":"10.3390\/e11030513","article-title":"Scale-based Gaussian coverings: Combining intra and inter mixture models in image segmentation","volume":"11","author":"Murtagh","year":"2009","journal-title":"Entropy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14397","DOI":"10.3390\/s121114397","article-title":"A coded aperture compressive imaging array and its visual detection and tracking algorithms for surveillance systems","volume":"12","author":"Chen","year":"2012","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.3390\/rs5052145","article-title":"SAR images statistical modeling and classification based on the mixture of alpha-stable distributions","volume":"5","author":"Peng","year":"2013","journal-title":"Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"775","DOI":"10.3390\/s100100775","article-title":"Statistical modeling of SAR images: A survey","volume":"10","author":"Gao","year":"2010","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TGRS.2004.842108","article-title":"Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering","volume":"43","author":"Kersten","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1109\/TGRS.2011.2140120","article-title":"Automated non-Gaussian clustering of polarimetric synthetic aperture radar images","volume":"49","author":"Doulgeris","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1214\/aoms\/1177704250","article-title":"Statistical analysis based on a certain multivariate complex Gaussian distribution (An introduction)","volume":"34","author":"Goodman","year":"1963","journal-title":"Ann. Math. Stat"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/TGRS.2010.2087763","article-title":"Nonlocal filtering for polarimetric SAR data: A pretest approach","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1109\/TGRS.2011.2164085","article-title":"Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty","volume":"50","author":"Yu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_21","first-page":"341","article-title":"Low-rank kernel learning with Bregman matrix divergences","volume":"10","author":"Kulis","year":"2009","journal-title":"J. Mach. Learn. Res"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1109\/TGRS.2003.817795","article-title":"A new polarimetric classification approach evaluated for agricultural crops","volume":"41","author":"Hoekman","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/LGRS.2008.923262","article-title":"Fisher distribution for texture modeling of polarimetric SAR data","volume":"5","author":"Bombrun","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/TGRS.2010.2060730","article-title":"Hierarchical segmentation of polarimetric SAR images using heterogeneous clutter models","volume":"49","author":"Bombrun","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kersten, P.R., Anfinsen, S.N., and Doulgeris, A.P. (2012, January 22\u201327). The Wishart-Kotz Classifier for Multilook Polarimetric SAR Data. Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350758"},{"key":"ref_26","unstructured":"Skriver, H., Dall, J., Le Toan, T., Quegan, S., Ferro-Famil, L., Pottier, E., Lumsdon, P., and Moshammer, R. (2005, January 17\u201321). Agriculture Classification Using PolSAR Data. Frascati, Italy."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/5\/3770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:10:47Z","timestamp":1760217047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/5\/3770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,4,28]]},"references-count":26,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2014,5]]}},"alternative-id":["rs6053770"],"URL":"https:\/\/doi.org\/10.3390\/rs6053770","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,4,28]]}}}