{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:58:18Z","timestamp":1753887498430,"version":"3.41.2"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":329,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009950","name":"Ministry of Education","doi-asserted-by":"publisher","award":["201802123151","201902084029"],"award-info":[{"award-number":["201802123151","201902084029"]}],"id":[{"id":"10.13039\/100009950","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.<\/jats:p>","DOI":"10.1155\/2021\/2392642","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T17:05:09Z","timestamp":1637946309000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A SAR Target Recognition Method via Combination of Multilevel Deep Features"],"prefix":"10.1155","volume":"2021","author":[{"given":"Junhua","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6088-4723","authenticated-orcid":false,"given":"Yuan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2016.2611492"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2013.0027"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.jrs.10.046006"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/9680465"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2019.04.014"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/09205071.2018.1495580"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/taes.2012.6178042"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"MishraA. K. Validation of PCA and LDA for SAR ATR Proceedings of the IEEE TENCON Conference November 2008 Hyderabad India 1\u20136.","DOI":"10.1109\/TENCON.2008.4766807"},{"key":"e_1_2_10_9_2","doi-asserted-by":"crossref","unstructured":"MishraA. K.andMotaungT. Application of linear and nonlinear PCA to SAR ATR Proceedings of the IEEE 25th International Conference Radioelektronika (RADIOELEKTRONIKA) April 2015 Pardubice Czech Republic 349\u2013354.","DOI":"10.1109\/RADIOELEK.2015.7129065"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2014.0407"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs10020211"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/taes.2013.110769"},{"key":"e_1_2_10_13_2","unstructured":"XiongW. CaoL. andHaoZ. Combining wavelet invariant moments and relevance vector machine for SAR target recognition Proceedings of the IET International Radar Conference April 2009 Guilin China 1\u20134."},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/jstars.2015.2436694"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2901317"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2941397"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/83.552098"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.007"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/jstars.2017.2671919"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/7.937475"},{"key":"e_1_2_10_21_2","doi-asserted-by":"crossref","unstructured":"TisonC. PourthieN. andSouyrisJ. Target recognition in SAR images with support vector machines (SVM) Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium July 2007 Barcelona Spain 456\u2013459.","DOI":"10.1109\/IGARSS.2007.4422829"},{"key":"e_1_2_10_22_2","doi-asserted-by":"crossref","unstructured":"DemirhanM. E.andSalor\u00d6. Classification of targets in SAR images using SVM and k-NN techniques Proceedings of the 2016 24th Signal Processing and Communication Application Conference (SIU) May 2016 Zonguldak Turkey 1581\u20131584.","DOI":"10.1109\/SIU.2016.7496056"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.01.033"},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"ThiagaraianmJ. J. RamamurthyK. N. KneeP. SpaniasA. andBerishaV. Sparse representations for automatic target classification in SAR images Proceedings of the 4th International Symposium on Communications Control and Signal Processing March 2010 Limassol Cyprus 1\u20134.","DOI":"10.1109\/ISCCSP.2010.5463416"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/app6010026"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2018.02.012"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2032645"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9063419"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/mgrs.2017.2762307"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17010192"},{"key":"e_1_2_10_31_2","doi-asserted-by":"crossref","unstructured":"MorganD. E. Deep convolutional neural networks for ATR from SAR imagery Proceedings of the SPIE May 2015 Baltimore MD USA 1\u201313.","DOI":"10.1117\/12.2176558"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2016.2551720"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2869289"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2906564"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/taes.2018.2864809"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs10060819"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.jrs.8.083613"},{"key":"e_1_2_10_38_2","unstructured":"XinY. KuanL. andJiaoL. SAR automatic target recognition based on classifiers fusion Proceedings of the International Workshop on Multi-Platform\/Multi-Sensor Remote Sensing and Mapping January 2011 Xiamen China 1\u20135."},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.1186\/1687-1499-2013-39"},{"key":"e_1_2_10_40_2","doi-asserted-by":"crossref","unstructured":"SrinivasU.andMongaV. Meta-classifiers for exploiting feature dependence in automatic target recognition Proceedings of the IEEE Radar Conference May 2011 Kansas City MO USA 147\u2013151.","DOI":"10.1109\/RADAR.2011.5960517"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2010.0319"},{"key":"e_1_2_10_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_10_43_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition June 2016 LasVegas NV USA 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_10_44_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4428964"},{"key":"e_1_2_10_45_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6649970"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/2392642.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/2392642.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/2392642","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T10:39:58Z","timestamp":1722940798000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/2392642"}},"subtitle":[],"editor":[{"given":"Bai Yuan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/2392642"],"URL":"https:\/\/doi.org\/10.1155\/2021\/2392642","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-10-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2392642"}}