{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T02:52:43Z","timestamp":1783824763468,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,27]],"date-time":"2018-01-27T00:00:00Z","timestamp":1517011200000},"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, we present a novel method for ship classification in synthetic aperture radar (SAR) images. The proposed method consists of feature extraction and classifier training. Inspired by SAR-HOG feature in automatic target recognition, we first design a novel feature named MSHOG by improving SAR-HOG, adapting it to ship classification, and employing manifold learning to achieve dimensionality reduction. Then, we train the classifier and dictionary jointly in task-driven dictionary learning (TDDL) framework. To further improve the performance of TDDL, we enforce structured incoherent constraints on it and develop an efficient algorithm for solving corresponding optimization problem. Extensive experiments performed on two datasets with TerraSAR-X images demonstrate that the proposed method, MSHOG feature and TDDL with structured incoherent constraints, outperforms other existing methods and achieves state-of-art performance.<\/jats:p>","DOI":"10.3390\/rs10020190","type":"journal-article","created":{"date-parts":[[2018,1,29]],"date-time":"2018-01-29T07:46:20Z","timestamp":1517211980000},"page":"190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Ship Classification Based on MSHOG Feature and Task-Driven Dictionary Learning with Structured Incoherent Constraints in SAR Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Huiping","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengli","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TGRS.2010.2071879","article-title":"Ship Surveillance with TerraSAR-X","volume":"49","author":"Brusch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Osman, H.M., Pan, L., Blostein, S.D., and Gagnon, L. (1997, January 24). Classification of ships in airborne SAR imagery using backpropagation neural networks. Proceedings of the Radar Processing, Technology, and Applications II, San Diego, CA, USA.","DOI":"10.1117\/12.279464"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1109\/TGRS.2011.2112371","article-title":"Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic","volume":"49","author":"Margarit","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/LGRS.2012.2237377","article-title":"Merchant Vessel Classification Based on Scattering Component Analysis for COSMO-SkyMed SAR Images","volume":"10","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/LGRS.2016.2514482","article-title":"Ship Classification Based on Superstructure Scattering Features in SAR Images","volume":"13","author":"Jiang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xing, X.W., Ji, K.F., Chen, W.T., Zou, H.X., and Sun, J.X. (2014, January 22\u201323). Superstructure scattering distribution based ship recognition in TerraSAR-X imagery. Proceedings of the IOP Conference Series: Earth and Environmental Science, Kuala Lumpur, Malaysia.","DOI":"10.1088\/1755-1315\/17\/1\/012119"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1049\/el.2016.4598","article-title":"2D comb feature for analysis of ship classification in high-resolution SAR imagery","volume":"53","author":"Leng","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lang, H., and Meng, J. (2014). Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery. J. Appl. Remote Sens., 8.","DOI":"10.1117\/1.JRS.8.083623"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2015.2506570","article-title":"Ship Classification in SAR Image by Joint Feature and Classifier Selection","volume":"13","author":"Lang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, W.T., Ji, K.F., Xing, X.W., Zou, H.X., and Sun, H. (2012, January 16\u201318). Ship recognition in high resolution SAR imagery based on feature selection. Proceedings of the International Conference on Computer Vision in Remote Sensing, Xiamen, China.","DOI":"10.1109\/CVRS.2012.6421279"},{"key":"ref_11","unstructured":"Fernandez Arguedas, V., Velotto, D., Tings, B., Greidanus, H., and Bentes da Silva, C.A. (2016, January 13\u201314). Ship classification in high and very high resolution satellite SAR imagery. Proceedings of the Security Research Conference, 11th Future Security, Berlin, Germany."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bentes, C., Velotto, D., and Lehner, S. (2015, January 26\u201331). Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326627"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/JOE.2017.2767106","article-title":"Ship Classification in TerraSAR-X Images with Convolutional Neural Networks","volume":"43","author":"Bentes","year":"2017","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_14","unstructured":"Santamaria, C., Stasolla, M., Argentieri, P., Alvarez, M., and Greidanus, H. (2015). Sentinel-1 Maritime Surveillance. Testing and Experiences with Long-Term Monitoring, Publications Office of the European Union. JRC Science and Policy Reports."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Song, S., Xu, B., and Yang, J. (2016). SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature. Remote Sens., 8.","DOI":"10.3390\/rs8080683"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11263-005-4939-z","article-title":"Unsupervised learning of image manifolds by semidefinite programming","volume":"70","author":"Weinberger","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","article-title":"Laplacian eigenmaps for dimensionality reduction and data representation","volume":"15","author":"Belkin","year":"2003","journal-title":"Neural Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5591","DOI":"10.1073\/pnas.1031596100","article-title":"Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data","volume":"100","author":"Donoho","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_21","unstructured":"Brand, M. (2003). Charting a manifold. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1137\/S1064827502419154","article-title":"Principal manifolds and nonlinear dimensionality reduction via tangent space alignment","volume":"26","author":"Zhang","year":"2004","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1109\/TIT.2010.2040894","article-title":"Model-based compressive sensing","volume":"56","author":"Baraniuk","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1137\/S003614450037906X","article-title":"Atomic decomposition by basis pursuit","volume":"43","author":"Chen","year":"2001","journal-title":"SIAM Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via sparse representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, J.B., and Yang, M.H. (2010, January 13\u201318). Fast sparse representation with prototypes. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539919"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral image classification using dictionary-based sparse representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TGRS.2012.2201730","article-title":"Hyperspectral image classification via kernel sparse representation","volume":"51","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TAES.2012.6237604","article-title":"Multi-view automatic target recognition using joint sparse representation","volume":"48","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/LGRS.2013.2262073","article-title":"Ship Classification in TerraSAR-X Images with Feature Space Based Sparse Representation","volume":"10","author":"Xing","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"rmK-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ramirez, I., Sprechmann, P., and Sapiro, G. (2010, January 13\u201318). Classification and clustering via dictionary learning with structured incoherence and shared features. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539964"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2008, January 23\u201328). Discriminative learned dictionaries for local image analysis. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587652"},{"key":"ref_36","unstructured":"Mairal, J., Ponce, J., Sapiro, G., Zisserman, A., and Bach, F.R. (2009). Supervised dictionary learning. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/TIP.2009.2022459","article-title":"Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization","volume":"18","author":"Sapiro","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TPAMI.2011.156","article-title":"Task-driven dictionary learning","volume":"34","author":"Mairal","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Elad, M. (2010). Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer.","DOI":"10.1007\/978-1-4419-7011-4"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_41","unstructured":"Alpaydin, E. (1988). Introduction to Machine Learning, Pitman."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shawe-Taylor, J., and Cristianini, N. (2005). Kernel Methods for Pattern Analysis, China Machine Press.","DOI":"10.1017\/CBO9780511809682"},{"key":"ref_43","unstructured":"Bradley, D.M., and Bagnell, J.A. (2008, January 8\u201311). Differentiable sparse coding. Proceedings of the International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, J., Yu, K., and Huang, T. (2010, January 13\u201318). Supervised translation-invariant sparse coding. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539958"},{"key":"ref_45","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1080\/10556789908805765","article-title":"CSDP, A C library for semidefinite programming","volume":"11","author":"Borchers","year":"1999","journal-title":"Optim. Methods Softw."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez, I., Lecumberry, F., and Sapiro, G. (2009, January 13\u201316). Universal priors for sparse modeling. Proceedings of the 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, The Netherlands.","DOI":"10.1109\/CAMSAP.2009.5413302"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/TIP.2013.2290593","article-title":"Learning category-specific dictionary and shared dictionary for fine-grained image categorization","volume":"23","author":"Gao","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4457","DOI":"10.1109\/TGRS.2015.2399978","article-title":"Task-driven dictionary learning for hyperspectral image classification with structured sparsity constraints","volume":"53","author":"Sun","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1109\/TGRS.2014.2335177","article-title":"Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","first-page":"27","article-title":"LIBSVM: A library for support vector machines","volume":"Volume 2","author":"Chang","year":"2011","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:52:49Z","timestamp":1760194369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,27]]},"references-count":51,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020190"],"URL":"https:\/\/doi.org\/10.3390\/rs10020190","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,27]]}}}