{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:02:31Z","timestamp":1760227351980,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T00:00:00Z","timestamp":1650931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2039"],"award-info":[{"award-number":["U21B2039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic aperture radar (SAR) target discrimination is an important stage that distinguishes targets from clutters in the radar automatic target recognition field. However, in complex SAR scenes, the performance of some traditional discriminators will degrade. As an effective tool for one-class classification (OCC), the max-margin one-class classifier has attracted much attention for SAR target discrimination, as it can effectively reduce the impact of multiple clutters. However, the performance of the max-margin one-class classifier is very sensitive to the values of kernel parameters. To solve the problem, this paper proposes an adaptive max-margin one-class classifier for SAR target discrimination in complex scenes. In a max-margin one-class classifier with a suitable kernel parameter, the distance between a sample and classification boundary satisfies a certain geometric relationship, i.e., edge samples in input space are transformed to the region in the kernel space close to boundary, while interior samples in input space are transformed to the region in the kernel space far away from boundary. Therefore, we define the information entropy of samples in the kernel space to measure the distance between samples and classification boundary. To automatically obtain the optimal kernel parameter of the max-margin one-class classifier, the edge and interior samples in the input space are first selected, and then the parameter optimization is performed by minimizing information entropy of interior samples and simultaneously maximizing the information entropy of edge samples. Experimental results of the synthetic datasets and measured synthetic aperture radar (SAR) datasets validate the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/rs14092078","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T21:37:53Z","timestamp":1651009073000},"page":"2078","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Max-Margin One-Class Classifier for SAR Target Discrimination in Complex Scenes"],"prefix":"10.3390","volume":"14","author":[{"given":"Leiyao","family":"Liao","sequence":"first","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-0022","authenticated-orcid":false,"given":"Lan","family":"Du","sequence":"additional","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[{"name":"The National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TAES.2017.2649160","article-title":"Automatic target recognition of military vehicles with Krawtchouk moments","volume":"53","author":"Clemente","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1049\/iet-rsn.2014.0296","article-title":"Pseudo-Zernike-based multi-pass automatic target recognition from multi-channel synthetic aperture radar","volume":"9","author":"Clemente","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TGRS.2010.2052623","article-title":"An improved scheme for target discrimination in high-resolution SAR images","volume":"49","author":"Gao","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Novak, L.M., Owirka, G.J., and Brower, W.S. (1998, January 1\u20134). An Efficient Multi-Target SAR ATR Algorithm. Proceedings of the Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No. 98CH36284), Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.1998.750815"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6372","DOI":"10.1109\/JSTARS.2021.3089238","article-title":"Unsupervised Domain Adaptation for SAR Target Detection","volume":"14","author":"Shi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1109\/TGRS.2019.2953936","article-title":"Saliency-guided single shot multibox detector for target detection in SAR images","volume":"58","author":"Du","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/LGRS.2018.2867242","article-title":"SAR target detection based on SSD with data augmentation and transfer learning","volume":"16","author":"Wang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1109\/JSTARS.2018.2850043","article-title":"Superpixel-level target discrimination for high-resolution SAR images in complex scenes","volume":"11","author":"Wang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1109\/LGRS.2017.2752763","article-title":"SAR target discrimination based on BOW model with sample-reweighted category-specific and shared dictionary learning","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1109\/JSEN.2018.2791947","article-title":"Target discrimination for SAR ATR based on scattering center feature and K-center one-class classification","volume":"18","author":"Li","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_11","first-page":"5211212","article-title":"Multiscale CNN based on component analysis for SAR ATR","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8386","DOI":"10.1109\/JSTARS.2021.3104267","article-title":"Boosting Lightweight CNNs Through Network Pruning and Knowledge Distillation for SAR Target Recognition","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5266","DOI":"10.1016\/j.csda.2006.09.032","article-title":"Modelling nonlinear count time series with local mixtures of Poisson autoregressions","volume":"51","author":"Carvalho","year":"2007","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.neucom.2013.07.002","article-title":"Boundary detection and sample reduction for one-class support vector machines","volume":"123","author":"Zhu","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_15","first-page":"582","article-title":"SV estimation of a distribution\u2019s support","volume":"41","author":"Williamson","year":"2000","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s10846-007-9146-9","article-title":"Real-time automated visual inspection using mobile robots","volume":"49","author":"Nehmzow","year":"2007","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deng, H., and Xu, R. (April, January 1). Model Selection for Anomaly Detection in Wireless ad hoc Networks. Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, Honolulu, HI, USA.","DOI":"10.1109\/CIDM.2007.368922"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.asoc.2012.11.005","article-title":"A modified support vector data description based novelty detection approach for machinery components","volume":"13","author":"Wang","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1109\/TCYB.2014.2340433","article-title":"Parameter selection of Gaussian kernel for one-class SVM","volume":"45","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_20","first-page":"3","article-title":"An overview of automatic target recognition","volume":"6","author":"Dudgeon","year":"1993","journal-title":"Linc. Lab. J."},{"key":"ref_21","first-page":"185","article-title":"Sequential minimal optimization: A fast algorithm for training support vector machines","volume":"Volume 208","author":"Platt","year":"1998","journal-title":"Advances in Kernel Methods-Support Vector Learning"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1109\/TPAMI.2010.188","article-title":"Selecting critical patterns based on local geometrical and statistical information","volume":"33","author":"Li","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/LGRS.2010.2098842","article-title":"Anomaly detection in hyperspectral images based on an adaptive support vector method","volume":"8","author":"Khazai","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","unstructured":"Sain, S.R. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1016\/j.neucom.2008.05.003","article-title":"Minimum spanning tree based one-class classifier","volume":"72","author":"Juszczak","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S0925-2312(98)00031-9","article-title":"Self-organizing maps of symbol strings","volume":"21","author":"Kohonen","year":"1998","journal-title":"Neurocomputing"},{"key":"ref_28","first-page":"905","article-title":"Robust novelty detection with single-class MPM","volume":"15","author":"Ghaoui","year":"2002","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liao, L., Du, L., and Guo, Y. (2021). Semi-Supervised SAR Target Detection Based on an Improved Faster R-CNN. Remote Sens., 14.","DOI":"10.3390\/rs14010143"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guo, Y., Du, L., and Lyu, G. (2021). SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size. Remote Sens., 13.","DOI":"10.3390\/rs13214202"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wei, D., Du, Y., Du, L., and Li, L. (2021). Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism. Remote Sens., 13.","DOI":"10.3390\/rs13142686"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2078\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:01:18Z","timestamp":1760137278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,26]]},"references-count":32,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092078"],"URL":"https:\/\/doi.org\/10.3390\/rs14092078","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,4,26]]}}}