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Firstly, the VGGNet model pretrained on the ImageNet dataset is fine-tuned to capture semantic information of the specific dual-band ship dataset. Secondly, the pretrained and fine-tuned VGGNet models are used to extract low-level, middle-level, and high-level convolutional features of each band image, and a number of improved recursive neural networks with random weights are exploited to reduce feature dimension and learn feature representation. Thirdly, to improve the quality of feature fusion, multilevel and multilayer convolutional features of dual-band images are concatenated to fuse hierarchical information and spectral information. Finally, the fused feature vector is fed into a linear support vector machine for dual-band maritime ship category recognition. Experimental results on the public dual-band maritime ship dataset show that multilayer convolution feature fusion outperforms single-layer convolution feature by about 2% mean per-class classification accuracy for single-band image, dual-band images perform better than single-band image by about 2.3%, and the proposed method achieves the best accuracy of 89.4%, which is higher than the state-of-the-art method by 1.2%.<\/jats:p>","DOI":"10.1155\/2020\/8891018","type":"journal-article","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T01:50:40Z","timestamp":1606960240000},"page":"1-16","source":"Crossref","is-referenced-by-count":6,"title":["Dual-Band Maritime Imagery Ship Classification Based on Multilayer Convolutional Feature Fusion"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6197-0348","authenticated-orcid":true,"given":"Xiaohua","family":"Qiu","sequence":"first","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"},{"name":"School of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3009-279X","authenticated-orcid":true,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}]},{"given":"Lin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Guangmang","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Liqiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"},{"name":"School of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trit.2017.03.001"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2850281"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.041"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.3390\/app9102153"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/152"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.09.015"},{"key":"7","first-page":"1097","article-title":"Imagenet classication with deep convolutional neural networks","volume-title":"Advances in Neural Information Processing Systems","author":"A. 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