{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:22:06Z","timestamp":1779888126949,"version":"3.53.1"},"reference-count":30,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"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":["11590770-4"],"award-info":[{"award-number":["11590770-4"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep learning based methods have achieved state-of-the-art results on the task of ship type classification. However, most existing ship type classification algorithms take time\u2013frequency (TF) features as input, the underlying discriminative information of these features has not been explored thoroughly. This paper proposes a novel feature optimization method which is designed to minimize an objective function aimed at increasing inter-class and reducing intra-class feature distance for ship type classification. The objective function we design is able to learn a center for each class and make samples from the same class closer to the corresponding center. This ensures that the features maximize underlying discriminative information involved in the data, particularly for some targets that usually confused by the conventional manual designed feature. Results on the dataset from a real environment show that the proposed feature optimization approach outperforms traditional TF features.<\/jats:p>","DOI":"10.3390\/s20185429","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T09:40:56Z","timestamp":1600767656000},"page":"5429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A Feature Optimization Approach Based on Inter-Class and Intra-Class Distance for Ship Type Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6872-3924","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"first","affiliation":[{"name":"Key laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Ren","sequence":"additional","affiliation":[{"name":"Key laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Xu","sequence":"additional","affiliation":[{"name":"Key laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"A2","DOI":"10.1051\/0004-6361\/201220873","article-title":"LOFAR: The low-frequency array","volume":"556","author":"Wise","year":"2013","journal-title":"Astron. Astrophys."},{"key":"ref_2","first-page":"206","article-title":"Classification and Recognition of DEMON Spectrum of Ship Noise by Support Vector Machine","volume":"29","author":"Dai","year":"2010","journal-title":"J. Appl. Acoust."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1121\/1.4900181","article-title":"The classification of underwater acoustic target signals based on wave structure and support vector machine","volume":"136","author":"Meng","year":"2014","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_4","first-page":"717","article-title":"Sonar signal detection and classification using artificial neural networks","volume":"2","author":"Ward","year":"2000","journal-title":"Can. Conf. Electr. Comput. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, C., Huang, Z., Xu, J., and Yan, Y. (2018, January 22\u201325). Underwater target classification using deep learning. Proceedings of the OCEANS 2018 MTS\/IEEE Charleston, Charleston, SC, USA.","DOI":"10.1109\/OCEANS.2018.8604906"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Mu, L., Peng, Y., Qiu, M., Yang, X., Hu, C., and Zhang, F. (2016, January 9\u201311). Study on modulation spectrum feature extraction of ship radiated noise based on auditory model. Proceedings of the 2016 IEEE\/OES China Ocean Acoustics, Harbin, China.","DOI":"10.1109\/COA.2016.7535765"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/48.180304","article-title":"A neural network based hybrid system for detection, characterization, and classification of short-duration oceanic signals","volume":"17","author":"Ghosh","year":"2002","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_9","first-page":"361","article-title":"Underwater Target Recognition Based on Wavelet Packet Energy Spectrum and Support Vector Machine","volume":"36","author":"Liu","year":"2012","journal-title":"J. Wuhan Univ. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.dt.2012.09.001","article-title":"Bark-wavelet Analysis and Hilbert\u2013Huang Transform for Underwater Target Recognition","volume":"9","author":"Zeng","year":"2013","journal-title":"Def. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. ECCV, Springer.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_12","unstructured":"Hermans, A., Beyer, L., and Leibe, B. (2017). In defense of the triplet loss for person re-identification. arXiv."},{"key":"ref_13","unstructured":"Ranjan, R., Castillo, C.D., and Chellappa, R. (2017). L2-constrained Softmax Loss for Discriminative Face Verification. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tabassum, M.N., and Ollila, E. (2018, January 15\u201320). Compressive regularized discriminant analysis of high-dimensional data with applications to microarray studies. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462328"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Peddinti, V., Povey, D., and Khudanpur, S. (2015, January 6\u201310). A time delay neural network architecture for efficient modeling of long temporal contexts. Proceedings of the 16th Annual Conference of the International Speech Communication Association, Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-647"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"EL162","DOI":"10.1121\/1.2908406","article-title":"Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion","volume":"123","author":"Huang","year":"2008","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.phycom.2008.09.001","article-title":"Overview of channel models for underwater wireless communication networks","volume":"1","author":"Domingo","year":"2008","journal-title":"Phys. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, X., Zhou, Y., Zhou, Z., Bai, S., and Bai, X. (2018, January 18\u201322). Triplet-center loss for multi-view 3d object retrieval. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00208"},{"key":"ref_20","unstructured":"Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20\u201325). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pham, A.T., Raich, R., and Fern, X.Z. (2018, January 15\u201320). Discriminative Clustering with Cardinality Constraints. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461842"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/BF01246098","article-title":"An entropy criterion for assessing the number of clusters in a mixture model","volume":"13","author":"Celeux","year":"1996","journal-title":"J. Classif."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.apacoust.2016.06.008","article-title":"ShipsEar: An underwater vessel noise database","volume":"113","year":"2016","journal-title":"Appl. Acoust."},{"key":"ref_26","unstructured":"Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., and Schwarz, P. (2011, January 11\u201315). The kaldi speech recognition toolkit. Proceedings of the IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, Hilton Waikoloa Village, Big Island, HI, USA."},{"key":"ref_27","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: A system for largescale machine learning. arXiv."},{"key":"ref_28","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2012, May 08). Deep Sparse Rectifier Neural Networks. International Conference on Artificial Intelligence and Statistics. Available online: Http:\/\/www.jmlr.org\/proceedings\/papers\/v15\/glorot11a\/glorot11a.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2018, January 4\u20136). Improved Adam Optimizer for Deep Neural Networks. Proceedings of the 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS), Banff, AB, Canada.","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"ref_30","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5429\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:12:23Z","timestamp":1760177543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":30,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185429"],"URL":"https:\/\/doi.org\/10.3390\/s20185429","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,22]]}}}