{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:55:33Z","timestamp":1779202533572,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,9]],"date-time":"2017-11-09T00:00:00Z","timestamp":1510185600000},"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>The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.<\/jats:p>","DOI":"10.3390\/rs9111150","type":"journal-article","created":{"date-parts":[[2017,11,9]],"date-time":"2017-11-09T11:33:02Z","timestamp":1510227182000},"page":"1150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Exploiting Multi-View SAR Images for Robust Target Recognition"],"prefix":"10.3390","volume":"9","author":[{"given":"Baiyuan","family":"Ding","sequence":"first","affiliation":[{"name":"Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongjian","family":"Wen","sequence":"additional","affiliation":[{"name":"Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,9]]},"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":"Mishra, A.K. (, 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_3","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_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/JSTARS.2015.2436694","article-title":"SAR target recognition via joint sparse representation of monogenic signal","volume":"8","author":"Dong","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","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_8","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_9","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_10","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_11","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_12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2013.09.003","article-title":"Radargrammetric registration of airborne multi-aspect SAR data of urban areas","volume":"86","author":"Schmitt","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2016.2561021","article-title":"Data fusion and remote sensing: An ever-growing relationship","volume":"4","author":"Schmitt","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Brendel, G., and Horowitz, L. (2000, January 24). Benefits of aspect diversity for SAR ATR: Fundamental and experimental results. Proceedings of the SPIE, Orlando, FL, USA.","DOI":"10.1117\/12.396367"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Brown, M.Z. (2003, January 12). Analysis of multiple-view Bayesian classification for SAR ATR. Proceedings of the SPIE, Orlando, FL, USA.","DOI":"10.1117\/12.487171"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bhanu, B., and Jones, G. (2003, January 1). Exploiting azimuthal variance of scatterers for multiple look SAR recognition. Proceedings of the SPIE, Riverside, CA, USA.","DOI":"10.1117\/12.478686"},{"key":"ref_17","unstructured":"Ettinger, G., and Snyder, W. (2002, January 1). Model-based fusion of multi-look SAR for ATR. Proceedings of the SPIE, Orlando, FL, USA."},{"key":"ref_18","unstructured":"Vespe, M., Baker, C.J., and Griffiths, H.D. (2006, January 24\u201327). Aspect dependent drivers for multi-perspective target classification. Proceedings of the IEEE Conference on Radar, Verona, NY, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"267","DOI":"10.2528\/PIER12100304","article-title":"Target recognition of multi-aspect SAR images with fusion strategies","volume":"134","author":"Huan","year":"2013","journal-title":"Prog. Electromagn. Res."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Tropp, J.A., Gilbert, A.C., and Strauss, M.J. (2006). Algorithms for simultaneous sparse approximation. EURASIP J. Adv. Appl. Signal Process., 589\u2013602.","DOI":"10.1016\/j.sigpro.2005.05.031"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1016\/j.sigpro.2011.01.012","article-title":"Surveying and comparing simultaneous sparse approximation (or group lasso) algorithms","volume":"91","author":"Rakotomamonjy","year":"2011","journal-title":"Signal Process."},{"key":"ref_23","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 (APSAR), Singapore.","DOI":"10.1109\/APSAR.2015.7306277"},{"key":"ref_24","first-page":"3713","article-title":"Automatic target recognition of SAR images based on global scattering center model","volume":"10","author":"Zhou","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.777371","article-title":"Recognition of articulated and occluded objects","volume":"21","author":"Jones","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1150\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:45Z","timestamp":1760208525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/11\/1150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,9]]},"references-count":26,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["rs9111150"],"URL":"https:\/\/doi.org\/10.3390\/rs9111150","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,9]]}}}