{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:22:55Z","timestamp":1773519775004,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"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":["62171383, 61771401, 61771401, 61571365"],"award-info":[{"award-number":["62171383, 61771401, 61771401, 61571365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Underwater target classification has been an important topic driven by its general applications. Convolutional neural network (CNN) has been shown to exhibit excellent performance on classifications especially in the field of image processing. However, when applying CNN and related deep learning models to underwater target classifications, the problems, including small sample size of underwater target and low complexity requirement, impose a great challenge. In this paper, we have proposed the modified DCGAN model to augment data for targets with small sample size. The data generated from the proposed model help to improve classification performance under imbalanced category conditions. Furthermore, we have proposed the S-ResNet model to obtain good classification accuracy while significantly reducing complexity of the model, and achieve a good tradeoff between classification accuracy and model complexity. The effectiveness of proposed models is verified through measured data from sea trial and lake tests.<\/jats:p>","DOI":"10.3390\/s22062293","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T22:15:04Z","timestamp":1647468904000},"page":"2293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Classification of Underwater Target Based on S-ResNet and Modified DCGAN Models"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhe","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi\u2019an 710072, China"}]},{"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi\u2019an 710072, China"}]},{"given":"Haiyan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, H., Shen, S., Yao, X., Sheng, M., and Wang, C. (2018). Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition. Sensors, 18.","DOI":"10.3390\/s18040952"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhu, P., Isaacs, J., Fu, B., and Ferrari, S. (2017, January 21\u201315). Deep learning feature extraction for target recognition and classification in underwater sonar images. Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, Australia.","DOI":"10.1109\/CDC.2017.8264055"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/72.846748","article-title":"Underwater target classification using wavelet packets and neural networks","volume":"11","author":"Yao","year":"2000","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jin, L., and Liang, H. (2017, January 19\u201322). Deep learning for underwater image recognition in small sample size situations. Proceedings of the OCEANS 2017, Aberdeen, UK.","DOI":"10.1109\/OCEANSE.2017.8084645"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1214301","DOI":"10.1155\/2018\/1214301","article-title":"Deep Learning Methods for Underwater Target Feature Extraction and Recognition","volume":"2018","author":"Hu","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, X., Yu, Y., and Niu, L. (2016, January 16\u201318). Deep learning-based recognition of underwater target. Proceedings of the 2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, China.","DOI":"10.1109\/ICDSP.2016.7868522"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.eswa.2007.07.021","article-title":"Improving classification performance of sonar targets by applying general regression neural network with PCA","volume":"35","author":"Erkmen","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","article-title":"Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data","volume":"29","author":"Khan","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_9","first-page":"264","article-title":"z-SVM: An SVM for Improved Classification of Imbalanced Data","volume":"Volume 4304","author":"Sattar","year":"2006","journal-title":"Australasian Joint Conference on Artificial Intelligence"},{"key":"ref_10","unstructured":"Perego, R., Sebastiani, F., Aslam, J.A., Ruthven, I., and Zobel, J. (2016, January 17\u201321). Distributional Random Oversampling for Imbalanced Text Classification. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy."},{"key":"ref_11","unstructured":"Goodfellow I., J., Abadie, J., Mirza, M., Xu, B., Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_12","unstructured":"Choi, E., Biswal, S., Malin, B.A., Duke, J., Stewart, W.F., and Sun, J. (2017). Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/0952813X.2019.1647560","article-title":"Deep learning-based framework for expansion, recognition and classification of underwater acoustic signal","volume":"32","author":"Jin","year":"2020","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, L., Sun, J., Sun, J., and Yu, J. (2021, January 12\u201314). HRRP Data Augmentation Using Generative Adversarial Networks. Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9390834"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, F., Song, Q., and Jin, G. (2018, January 12\u201314). Expansion of restricted sample for underwater acoustic signal based on generative adversarial networks. Proceedings of the Tenth International Conference on Graphics and Image Processing (ICGIP 2018), Chengdu, China.","DOI":"10.1117\/12.2524173"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kumari, N., Anwar, S., and Bhattacharjee, V. (2021, January 25\u201327). DCGAN based Pre-trained model for Image Reconstruction using ImageNet. Proceedings of the 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII),  Chennai, India.","DOI":"10.1109\/ICBSII51839.2021.9445128"},{"key":"ref_17","unstructured":"Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5\u201310). Improved Techniques for Training GANs. Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain."},{"key":"ref_18","unstructured":"Denton, E.L., Chintala, S., Szlam, A., and Fergus, R. (2015, January 7\u201312). Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_19","unstructured":"Reed, S.E., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H. (2016, January 19\u201324). Generative Adversarial Text to Image Synthesis. Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York, NY, USA."},{"key":"ref_20","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016, January 5\u201310). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_23","first-page":"504","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Am. Assoc. Adv. Science. Sci."},{"key":"ref_24","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, FL, USA."},{"key":"ref_25","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Movshovitz-Attias, Y., Yu, Q., Stumpe, M.C., Shet, V., Arnoud, S., and Yatziv, L. (2015, January 7\u201312). Ontological supervision for fine grained classification of Street View storefronts. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298778"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE\/CVF Conference Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2Net: A New Multi-Scale Backbone Architecture","volume":"43","author":"Gao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"N, I.F., S, H., and M, M. (2017, January 24\u201326). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:37:24Z","timestamp":1760135844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,16]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062293"],"URL":"https:\/\/doi.org\/10.3390\/s22062293","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,16]]}}}