{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:53:17Z","timestamp":1783101197225,"version":"3.54.6"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T00:00:00Z","timestamp":1525824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.<\/jats:p>","DOI":"10.3390\/s18051490","type":"journal-article","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T03:48:27Z","timestamp":1525924107000},"page":"1490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images"],"prefix":"10.3390","volume":"18","author":[{"given":"Atmane","family":"Khellal","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"},{"name":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Fei","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing 100081, China"},{"name":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, M.M., Choi, J., Daniilidis, K., Wolf, M.T., and Kanan, C. (2015, January 7\u201312). VAIS: A dataset for recognizing maritime imagery in the visible and infrared spectrums. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301291"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1117\/12.357157","article-title":"Automatic classification of ships from infrared (FLIR) images","volume":"Volume 3720","author":"Withagen","year":"1999","journal-title":"Signal Processing, Sensor Fusion, and Target Recognition VIII"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Teutsch, M., and Kr\u00fcger, W. (2010, January 3\u20135). Classification of small boats in infrared images for maritime surveillance. Proceedings of the International Waterside Security Conference (WSS), Carrara, Italy.","DOI":"10.1109\/WSSC.2010.5730289"},{"key":"ref_4","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). 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