{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:48:31Z","timestamp":1761648511966,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,19]],"date-time":"2018-01-19T00:00:00Z","timestamp":1516320000000},"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":["No. 31500897"],"award-info":[{"award-number":["No. 31500897"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds of the Central Universities","award":["No. 1301030696"],"award-info":[{"award-number":["No. 1301030696"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper proposes an automatic target recognition (ATR) method for synthetic aperture radar (SAR) images based on information-decoupled representation. A typical SAR image of a ground target can be divided into three parts: target region, shadow and background. From the aspect of SAR target recognition, the target region and shadow contain discriminative information. However, they also include some confusing information because of the similarities of different targets. The background mainly contains redundant information, which has little contribution to the target recognition. Because the target segmentation may impair the discriminative information in the target region, the relatively simpler shadow segmentation is performed to separate the shadow region for information decoupling. Then, the information-decoupled representations are generated, i.e., the target image, shadow and original image. The background is retained in the target image, which represents the coupling of target backscattering and background. The original image and generated target image are classified using the sparse representation-based classification (SRC). Then, their classification results are combined by a score-level fusion for target recognition. The shadow image is not used because of its lower discriminability and possible segmentation errors. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under both standard operating condition (SOC) and various extended operating conditions (EOCs). The proposed method can correctly classify 10 classes of targets with the percentage of correct classification (PCC) of 94.88% under SOC. With the PCCs of 93.15% and 75.03% under configuration variance and 45\u00b0 depression angle, respectively, the superiority of the proposed is demonstrated in comparison with other methods. The robustness of the proposed method to both uniform and nonuniform shadow segmentation errors is validated with the PCCs over 93%. Moreover, with the maximum average precision of 0.9580, the proposed method is more effective than the reference methods on outlier rejection.<\/jats:p>","DOI":"10.3390\/rs10010138","type":"journal-article","created":{"date-parts":[[2018,1,22]],"date-time":"2018-01-22T04:51:13Z","timestamp":1516596673000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Target Recognition in SAR Images Based on Information-Decoupled Representation"],"prefix":"10.3390","volume":"10","author":[{"given":"Ming","family":"Chang","sequence":"first","affiliation":[{"name":"School of Psychology, Shaanxi Normal University, Xi\u2019an 710062, China"}]},{"given":"Xuqun","family":"You","sequence":"additional","affiliation":[{"name":"School of Psychology, Shaanxi Normal University, Xi\u2019an 710062, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6014","DOI":"10.1109\/ACCESS.2016.2611492","article-title":"Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review","volume":"4","author":"Gill","year":"2016","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dabboor, M., Montpetit, B., Howell, S., and Haas, C. (2017). Improving sea ice characterization in dry ice winter conditions using polarmetric parameters from C- and l-Band SAR data. Remote Sens., 9.","DOI":"10.3390\/rs9121270"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gao, F., Liu, X., Dong, J.Y., Zhong, G.Q., and Jian, M.W. (2017). Change detection in SAR images based on deep Semi-NMF and SVD networks. Remote Sens., 9.","DOI":"10.3390\/rs9050435"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"646","DOI":"10.20965\/jdr.2017.p0646","article-title":"Machine learning based building damage mapping from the ALSO-2\/PALSAR-2 SAR imagery: Case study of 2016 Kumamoto earthquake","volume":"12","author":"Bai","year":"2017","journal-title":"J. Disaster Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2017.2772349","article-title":"A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks","volume":"15","author":"Bai","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TIE.2015.2498909","article-title":"A novel variable selection approach for redundant information elimination purpose of process control","volume":"63","author":"Li","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","first-page":"1932","article-title":"Class-specific feature selection with local geometric structure and discriminative information based on sparse similar samples","volume":"12","author":"Chen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6858","DOI":"10.1109\/TGRS.2014.2304298","article-title":"SAR image denoising via clustering-based principal component analysis","volume":"52","author":"Xu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2277512","article-title":"A tutorial on speckle reduction in synthetic aperture radar Images","volume":"1","author":"Argenti","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1109\/TIP.2015.2421440","article-title":"Classification on the monogenic scale space: Application to target recognition in SAR image","volume":"24","author":"Dong","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/LGRS.2012.2210385","article-title":"New discrimination features for SAR automatic target recognition","volume":"10","author":"Park","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/TAES.2013.110769","article-title":"Neighborhood geometric center scaling embedding for SAR ATR","volume":"50","author":"Huang","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"046006","DOI":"10.1117\/1.JRS.10.046006","article-title":"Target recognition in synthetic aperture radar images using binary morphological operations","volume":"10","author":"Ding","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1049\/iet-cvi.2013.0027","article-title":"Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moment features","volume":"8","author":"Amoon","year":"2014","journal-title":"IET Comput. Vis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/TAES.2012.6178042","article-title":"Classification via the shadow region in SAR imagery","volume":"48","author":"Papson","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2934","DOI":"10.1016\/j.na.2009.07.030","article-title":"SVM-based target recognition from synthetic aperture radar images using target region outline descriptors","volume":"71","author":"Anagnostopulos","year":"2009","journal-title":"Nonlinear Anal."},{"key":"ref_17","unstructured":"Cui, J.J., Gudnason, J., and Brookes, M. (2005, January 18\u201323). Automatic recognition of MSTAR targets using radar shadow and super resolution features. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, PA, USA."},{"key":"ref_18","unstructured":"Schumacher, R., and Schiller, J. (2005, January 9\u201312). Non-cooperative target identification of battlefield targets\u2014Classification results based on SAR images. Proceedings of the IEEE International Radar Conference, Arlington, VA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mishra, A.K. (2008, January 19\u201321). Validation of PCA and LDA for SAR ATR. Proceedings of the 2008 IEEE Region 10 Conference, Hyderabad, India.","DOI":"10.1109\/TENCON.2008.4766807"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1049\/iet-rsn.2014.0407","article-title":"Target recognition in synthetic aperture radar via non-negative matrix factorization","volume":"9","author":"Cui","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thiagarajan, J., Ramamurthy, K., Knee, P.P., Spanias, A., and Berisha, V. (2010, January 3\u20135). Sparse representation for automatic target classification in SAR images. Proceedings of the 2010 4th Communications, Control and Signal Processing (ISCCSP), Limassol, Cyprus.","DOI":"10.1109\/ISCCSP.2010.5463416"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Karine, A., Toumi, A., Khenchaf, A., and Hassouni, M.E. (2017, January 22\u201324). Saliency attention and sift keypoints combinations for automatic target recognition on MSTAR dataset. Proceedings of the International Conference on Advanced Technologies for Signal and Imaging Processing (ATSIP), Fez, Morocco.","DOI":"10.1109\/ATSIP.2017.8075558"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karine, A., Toumi, A., Khenchaf, A., and Hassouni, M.E. (2016, January 25\u201327). Visual salient sift keypoints descriptors for automatic target recognition. Proceedings of the 6th European Workshop on Visual Information Processing (EUVIP), Marseille, France.","DOI":"10.1109\/EUVIP.2016.7764596"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Song, S.L., 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_25","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.1109\/JSTARS.2017.2671919","article-title":"Target recognition in synthetic aperture radar images via matching of attributed scattering centers","volume":"10","author":"Ding","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.neucom.2016.09.007","article-title":"A robust similarity measure for attributed scattering center sets with application to SAR ATR","volume":"219","author":"Ding","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/LGRS.2017.2692386","article-title":"Data augmentation by multilevel reconstruction using attributed scattering center for SAR target recognition","volume":"14","author":"Ding","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"016010","DOI":"10.1117\/1.JRS.10.016010","article-title":"Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition","volume":"10","author":"Ding","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1049\/iet-rsn.2016.0357","article-title":"Decision fusion based on physically relevant features for SAR ATR","volume":"11","author":"Ding","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/7.937475","article-title":"Support vector machines for synthetic radar automatic target recognition","volume":"37","author":"Zhao","year":"2001","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.neucom.2013.01.033","article-title":"Decision fusion of sparse representation and support vector machine for SAR image target recognition","volume":"113","author":"Liu","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/LGRS.2017.2766225","article-title":"Target recognition in radar images using weighted statistical dictionary-based sparse representation","volume":"14","author":"Karine","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Song, H.B., Ji, K.F., Zhang, Y.S., Xing, X.W., and Zou, H.X. (2016). Sparse representation-based SAR image target classification on the 10-class MSTAR Data set. Appl. Sci., 6.","DOI":"10.3390\/app6010026"},{"key":"ref_35","first-page":"1685","article-title":"Target classification using the deep convolutional networks for SAR images","volume":"47","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TAES.2007.357120","article-title":"Adaptive boosting for SAR automatic target recognition","volume":"43","author":"Sun","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TAES.2013.120340","article-title":"SAR automatic target recognition using discriminative graphical models","volume":"50","author":"Srinivas","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1080\/2150704X.2017.1331052","article-title":"Target recognition in SAR images by exploiting the azimuth sensitivity","volume":"8","author":"Ding","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1080\/2150704X.2017.1346397","article-title":"Target recognition of SAR images based on multi-resolution representation","volume":"8","author":"Ding","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_41","unstructured":"(2015, April 05). The Air Force Moving and Stationary Target Recognition Database. Available online: http:\/\/www.sdms.afrl.af.mil\/datasets\/mstar\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/18.720533","article-title":"Information-theoretic image formation","volume":"44","author":"Sullivan","year":"1998","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_44","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Prentice Hall. [3rd ed.]."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.inffus.2006.03.001","article-title":"Robust automatic target recognition using learning classifier systems","volume":"8","author":"Ravichandran","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_46","unstructured":"Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P.S. (1993, January 1\u20133). Orthogonal matching pursuit: Recursive function approximation with application to wavelet decomposition. Proceedings of the 27th Asilomar Conference on Signal, System and Computers, Pacific Grove, CA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1109\/TSMCB.2009.2038493","article-title":"Robust classifier for data reduced via random projections","volume":"40","author":"Majumdar","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. B Cybern."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Doo, S., Smith, G., and Baker, C. (2015, January 1\u20134). Target classification performance as a function of measurement uncertainty. Proceedings of the 5th Asia-Pacific Conference on Synthetic Aperture Radar, Singapore.","DOI":"10.1109\/APSAR.2015.7306277"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ding, B.Y., and Wen, G.J. (2017). Exploiting multi-view SAR images for robust target recognition. Remote Sens., 9.","DOI":"10.3390\/rs9111150"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/TAES.2015.150027","article-title":"Open set recognition for automatic target classification with rejection","volume":"52","author":"Scherreik","year":"2016","journal-title":"IEEE Trans. Aerosp. Electron. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:51:49Z","timestamp":1760194309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,19]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["rs10010138"],"URL":"https:\/\/doi.org\/10.3390\/rs10010138","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,1,19]]}}}