{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:27:14Z","timestamp":1764174434008,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T00:00:00Z","timestamp":1509494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4164093"],"award-info":[{"award-number":["4164093"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61501018","61571033"],"award-info":[{"award-number":["61501018","61571033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Higher Education and High-Quality and World-Class Universities","award":["PY201619"],"award-info":[{"award-number":["PY201619"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZY1702"],"award-info":[{"award-number":["ZY1702"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016M600901"],"award-info":[{"award-number":["2016M600901"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Feature extraction using polarimetric synthetic aperture radar (PolSAR) images is of great interest in SAR classification, no matter if it is applied in an unsupervised approach or a supervised approach. In the supervised classification framework, a major group of methods is based on machine learning. Various machine learning methods have been investigated for PolSAR image classification, including neural network (NN), support vector machine (SVM), and so on. Recently, representation-based classifications have gained increasing attention in hyperspectral imagery, such as the newly-proposed sparse-representation classification (SRC) and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic SVM for remotely-sensed image processing. However, rare studies have been found to extend this representation-based NRS classification into PolSAR images. By the use of the NRS approach, a polarimetric feature vector-based PolSAR image classification method is proposed in this paper. The polarimetric SAR feature vector is constructed by the components of different target decomposition algorithms for each pixel, including those scattering components of Freeman, Huynen, Krogager, Yamaguchi decomposition, as well as the eigenvalues, eigenvectors and their consequential parameters such as entropy, anisotropy and mean scattering angle. Furthermore, because all these representation-based methods were originally designed to be pixel-wise classifiers, which only consider the separate pixel signature while ignoring the spatial-contextual information, the Markov random field (MRF) model is also introduced in our scheme. MRF can provide a basis for modeling contextual constraints. Two AIRSAR data in the Flevoland area are used to validate the proposed classification scheme. Experimental results demonstrate that the proposed method can reach an accuracy of around     99 %     for both AIRSAR data by randomly selecting 300 pixels of each class as the training samples. Under the condition that the training data ratio is more than     4 %    , it has better performance than the SVM, SVM-MRF and NRS methods.<\/jats:p>","DOI":"10.3390\/rs9111114","type":"journal-article","created":{"date-parts":[[2017,11,1]],"date-time":"2017-11-01T16:01:19Z","timestamp":1509552079000},"page":"1114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-2373","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Jun","family":"Ni","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Qiang","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3938-7033","authenticated-orcid":false,"given":"Zheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Yifan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Wen","family":"Hong","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A Tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/36.789621","article-title":"Unsupervised classification using polarimetric decomposition and the complex Wishart classifier","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1109\/36.964970","article-title":"Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR","volume":"39","author":"Lee","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/36.134086","article-title":"An empirical model and an inversion technique for radar scattering from bare soil surfaces","volume":"30","author":"Oh","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/36.406677","article-title":"Measuring soil moisture with imaging radars","volume":"33","author":"Dubois","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","unstructured":"Hajnsek, I. (2001). Inversion of Surface Parameters from Polarimetric SAR Data. [Ph.D. Thesis, Friedrich Schiller University of Jena (FSU)]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"095981","DOI":"10.1117\/1.JRS.9.095981","article-title":"Soil moisture change detection model for slightly rough surface based on interferometric phase","volume":"9","author":"Yin","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3160","DOI":"10.1109\/TGRS.2014.2369481","article-title":"Topography retrieval from single-pass POLSAR data based on the polarization-dependent intensity ratio","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4811","DOI":"10.1109\/TGRS.2017.2701813","article-title":"CFAR Ship Detection in Nonhomogeneous Sea Clutter Using Polarimetric SAR Data Based on the Notch Filter","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/JSTARS.2013.2269996","article-title":"A New Automatic Ship Detection Method Using L-Band Polarimetric SAR Imagery","volume":"7","author":"Wei","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TGRS.2015.2469691","article-title":"Classification of polarimetric SAR images based on modeling contextual information and using texture features","volume":"54","author":"Masjedi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/LGRS.2015.2487450","article-title":"Model-based decomposition with cross scattering for polarimetric SAR urban areas","volume":"12","author":"Xiang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TGRS.2007.907601","article-title":"An Unsupervised Segmentation with an Adaptive Number of Clusters Using the SPAN\/H\/\u03b1\/A Space and the Complex Wishart Clustering for Fully Polarimetric SAR Data Analysis","volume":"45","author":"Cao","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","unstructured":"Fukuda, S., and Hirosawa, H. (2001, January 9\u201313). Support vector machine classification of land cover: Application to polarimetric SAR data. Proceedings of the 2001 IEEE International Geoscience and Remote Sensing Symposium (IGARSS\u201901), Sydney, Australia."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1109\/JSTARS.2016.2532922","article-title":"Semisupervised Feature Extraction with Neighborhood Constraints for Polarimetric SAR Classification","volume":"9","author":"Liu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6714","DOI":"10.1080\/01431161.2017.1363437","article-title":"Classification of the Yellow River delta area using fully polarimetric SAR measurements","volume":"38","author":"Buono","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/36.789635","article-title":"Polarimetric SAR speckle filtering and its implication for classification","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1109\/TIP.2014.2307437","article-title":"Analysis, evaluation, and comparison of polarimetric SAR speckle filtering techniques","volume":"23","author":"Foucher","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","unstructured":"Huynen, J.R. (1970). Phenomenological Theory of Radar Targets. [Ph.D. Thesis, Technical University]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/TGRS.2007.897929","article-title":"Fitting a two-component scattering model to polarimetric SAR data from forests","volume":"45","author":"Freeman","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/36.142931","article-title":"Calibration of Stokes and scattering matrix format polarimetric SAR data","volume":"30","author":"Freeman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","unstructured":"Neumann, M., Ferro-Famil, L., and Pottier, E. (2009, January 26\u201330). A general model-based polarimetric decomposition scheme for vegetated areas. Proceedings of the International Workshop Science Applications SAR Polarimetry Polarimetric Inferometry (POLINSAR), Frascati, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1049\/el:19900979","article-title":"New decomposition of the radar target scattering matrix","volume":"26","author":"Krogager","year":"1990","journal-title":"Electron. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/LGRS.2006.869986","article-title":"A four-component decomposition of POLSAR images based on the coherency matrix","volume":"3","author":"Yamaguchi","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/LGRS.2006.873229","article-title":"On Huynen\u2019s decomposition of a Kennaugh matrix","volume":"3","author":"Yang","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Barnes, R.M. (1988, January 16\u201318). Roll-invariant decompositions for the polarization covariance matrix. Proceedings of the Polarimetry Technology Workshop, Redstone Arsenal, AL, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/LGRS.2012.2198612","article-title":"Supervised graph embedding for polarimetric SAR image classification","volume":"10","author":"Shi","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7158","DOI":"10.3390\/rs6087158","article-title":"Polarimetric contextual classification of PolSAR images using sparse representation and superpixels","volume":"6","author":"Feng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3923","DOI":"10.1109\/JSTARS.2014.2359459","article-title":"Fully polarimetric SAR image classification via sparse representation and polarimetric features","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/LGRS.2015.2493242","article-title":"SAR image classification via hierarchical sparse representation and multisize patch features","volume":"13","author":"Hou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/LGRS.2016.2597965","article-title":"Coastal zone classification with fully polarimetric SAR imagery","volume":"13","author":"Gou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3571","DOI":"10.3390\/rs4113571","article-title":"Improving Wishart classification of polarimetric SAR data using the Hopfield Neural Network optimization approach","volume":"4","author":"Pajares","year":"2012","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/TGRS.2013.2241773","article-title":"Nearest regularized subspace for hyperspectral classification","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/LGRS.2014.2325978","article-title":"Kernel Collaborative Representation With Tikhonov Regularization for Hyperspectral Image Classification","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","unstructured":"Zhang, L., Yang, M., and Feng, X. (2011, January 6\u201313). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Geng, J., Fan, J., Wang, H., Fu, A., and Hu, Y. (2016, January 10\u201315). Joint collaborative representation for polarimetric SAR image classification. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729793"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1109\/36.789624","article-title":"A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images","volume":"37","author":"Fukuda","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Schick, A., Bauml, M., and Stiefelhagen, R. (2012, January 16\u201321). Improving Foreground Segmentations with Probabilistic Superpixel Markov Random Fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238923"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2013.2263282","article-title":"Spectral-spatial classification of hyperspectral images based on hidden Markov random fields","volume":"52","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1109\/TGRS.2010.2097268","article-title":"Bayesian hyperspectral image segmentation with discriminative class learning","volume":"49","author":"Borges","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Boykov, Y.Y., and Jolly, M.P. (2001, January 7\u201314). Interactive graph cuts for optimal boundary and region segmentation of objects in ND images. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Vancouver, BC, Canada."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/LGRS.2013.2250905","article-title":"Hyperspectral image classification using Gaussian mixture models and Markov random fields","volume":"11","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","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_48","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1049\/el:19850018","article-title":"Target decomposition theorems in radar scattering","volume":"21","author":"Cloude","year":"1985","journal-title":"Electron. Lett."},{"key":"ref_50","unstructured":"Holm, W.A., and Barnes, R.M. (1988, January 20\u201321). On radar polarization mixed target state decomposition techniques. Proceedings of the IEEE National Radar Conference, Ann Arbor, MI, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1109\/JSTARS.2015.2513161","article-title":"Eigen-decomposition-based four-component decomposition for PolSAR data","volume":"9","author":"Zou","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","unstructured":"Touzi, R. (2004, January 20\u201324). Target scattering decomposition of one-look and multi-look SAR data using a new coherent scattering model: The TSVM. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS\u201904), Anchorage, AK, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1049\/iet-ipr.2012.0082","article-title":"Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system","volume":"7","author":"Khokher","year":"2013","journal-title":"IET Image Process."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TPAMI.2004.1262177","article-title":"What energy functions can be minimized via graph cuts?","volume":"26","author":"Kolmogorov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1114\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:49:08Z","timestamp":1760208548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,1]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["rs9111114"],"URL":"https:\/\/doi.org\/10.3390\/rs9111114","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2017,11,1]]}}}