{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:57:18Z","timestamp":1781110638196,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T00:00:00Z","timestamp":1515974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472306\uff0c91438201\uff0c61572383"],"award-info":[{"award-number":["61472306\uff0c91438201\uff0c61572383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2013CB329402"],"award-info":[{"award-number":["2013CB329402"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the fund for Foreign Scholars in University Research and Teaching Programs","award":["B07048"],"award-info":[{"award-number":["B07048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image classification method based on multilayer autoencoders and self-paced learning (SPL) is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs10010110","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T12:30:36Z","timestamp":1516019436000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach"],"prefix":"10.3390","volume":"10","author":[{"given":"Wenshuai","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2619-6481","authenticated-orcid":false,"given":"Shuiping","family":"Gou","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7038-5119","authenticated-orcid":false,"given":"Xiaofeng","family":"Li","sequence":"additional","affiliation":[{"name":"GST at NOAA\/NESDIS, College Park, MD 20740, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TGRS.2016.2514504","article-title":"POL-SAR image classification based on Wishart DBN and local spatial information","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/LGRS.2013.2247561","article-title":"Coastline extraction using dual-polarimetric COSMO-SkyMed PingPong mode SAR data","volume":"11","author":"Nunziata","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3994","DOI":"10.1080\/01431161.2014.916480","article-title":"Shoreline movement monitoring based on SAR images in Shanghai, China","volume":"35","author":"Ding","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"894","article-title":"Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images","volume":"13","author":"Ding","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Xiang, H., Liu, S., Zhuang, Z., and Zhang, N. (2016). A classification algorithm based on Cloude decomposition model for fully polarimetric SAR image. IOP Conf. Ser. Earth Environ. Sci., 46.","DOI":"10.1088\/1755-1315\/46\/1\/012060"},{"key":"ref_7","first-page":"7978","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_8","doi-asserted-by":"crossref","first-page":"7978","DOI":"10.1080\/2150704X.2014.978952","article-title":"Polarimetric SAR image classification by boosted multiple-kernel extreme learning machines with polarimetric and spatial features","volume":"35","author":"Du","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1109\/TGRS.2016.2631632","article-title":"A Fully Polarimetric SAR Imagery Classification Scheme for Mud and Sand Flats in Intertidal Zones","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","unstructured":"Xie, H., Wang, S., Liu, K., Lin, S., and Hou, B. (2014, January 13\u201318). Multilayer feature learning for polarimetric synthetic radar data classification. Proceedings of the IEEE Conference on International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_11","unstructured":"Lv, Q., Dou, Y., and Niu, X. (2014, January 13\u201318). Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data. Proceedings of the IEEE Conference on International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric SAR image classification using deep convolutional neural networks","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","unstructured":"Kumar, M.P., Packer, B., and Koller, D. (2010, January 6\u20139). Self-paced learning for latent variable models. Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Vancouver, BC, Canada."},{"key":"ref_14","unstructured":"Tang, Y., Yang, Y.B., and Gao, Y. (November, January 29). Self-paced dictionary learning for image classification. Proceedings of the ACM International Conference on Multimedia, Nara, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., and Leng, X. (2017). Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder. Sensors, 17.","DOI":"10.3390\/s17010192"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ranzato, M.A., and Szummer, M. (2008, January 5\u20139). Semi-supervised learning of compact document representations with deep networks. Proceedings of the International Conference on Machine Learning (ICML), Helsinki, Finland.","DOI":"10.1145\/1390156.1390256"},{"key":"ref_17","unstructured":"Huang, F.J., Boureau, Y.L., and LeCun, Y. (2007, January 12\u201322). Unsupervised learning of invariant feature hierarchies with applications to object recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MI, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1109\/JSTARS.2016.2553104","article-title":"Classification of polarimetric SAR image using multilayer autoencoders and superpixels","volume":"9","author":"Hou","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bengio, Y., and Louradour, J. (2009, January 14\u201318). Curriculum learning. Proceedings of the International Conference on Machine Learning (ICML), Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553380"},{"key":"ref_20","unstructured":"Meng, D.Y., Zhao, Q., and Jiang, L. (arXiv, 2015). What objective does self-paced learning indeed optimize?, arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rifai, S., Mesnil, G., Vincent, P., Muller, X., Bengio, Y., Dauphin, Y., and Glorot, X. (2011, January 5\u20139). Higher order contractive auto-encoder. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens, Greece.","DOI":"10.1007\/978-3-642-23783-6_41"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_23","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4143","DOI":"10.1109\/TGRS.2009.2023908","article-title":"Support vector machine for multifrequency SAR polarimetric data classification","volume":"47","author":"Lardeux","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","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_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"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."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Proc."},{"key":"ref_30","unstructured":"Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P.S. (1993, January 1\u20133). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Proceedings of the IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.isprsjprs.2008.12.008","article-title":"Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery","volume":"64","author":"Ainsworth","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1109\/TGRS.2013.2258675","article-title":"Integrating color features in polarimetric SAR image classification","volume":"52","author":"Uhlmann","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/JSTARS.2014.2320366","article-title":"A multi-polarization analysis of coastline extraction using X-band COSMO-SkyMed SAR data","volume":"7","author":"Buono","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/LGRS.2016.2597965","article-title":"Coastal zone classification with full-polarization SAR imagery","volume":"13","author":"Gou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. 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