{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:40:58Z","timestamp":1764978058497,"version":"3.46.0"},"reference-count":8,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The application of synthetic aperture radar (SAR) for ship and iceberg monitoring is important to carry out marine activities safely. The task of differentiating the two target classes, i.e. ship and iceberg, presents a challenge for operational scenarios. The dataset comprising SAR images of ship and iceberg poses a major challenge, as we are provided with a small number of labeled samples in the training set compared to a large number of unlabeled test samples. This paper proposes a semisupervised learning approach known as pseudolabeling to deal with the insufficient amount of training data. By adopting this approach, we make use of both labeled data (supervised learning) and unlabeled data (unsupervised learning) to build a robust convolutional neural network model that results in a superior binary classification performance of the proposed method.<\/jats:p>","DOI":"10.1515\/jisys-2018-0271","type":"journal-article","created":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T13:02:55Z","timestamp":1563973375000},"page":"1514-1522","source":"Crossref","is-referenced-by-count":3,"title":["Implementation of Improved Ship-Iceberg Classifier Using Deep Learning"],"prefix":"10.1515","volume":"29","author":[{"given":"Ankita","family":"Rane","sequence":"first","affiliation":[{"name":"Department of Computer Science , Birla Institute of Technology and Science (BITS) Pilani \u2013 Dubai Campus , Dubai , United Arab Emirates"}]},{"given":"Vadivel","family":"Sangili","sequence":"additional","affiliation":[{"name":"Professor in Department of Computer Science , Birla Institute of Technology and Science (BITS) Pilani \u2013 Dubai Campus , Dubai , United Arab Emirates"}]}],"member":"374","published-online":{"date-parts":[[2019,7,24]]},"reference":[{"key":"2025120523362752966_j_jisys-2018-0271_ref_001","unstructured":"C. Bentes, A. Frost, D. Velotto and B. Tings, Ship-iceberg discrimination with convolutional neural networks in high resolution SAR images, in: Processing of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar."},{"key":"2025120523362752966_j_jisys-2018-0271_ref_002","unstructured":"F. Chollet, Building powerful image classification models using very little data."},{"key":"2025120523362752966_j_jisys-2018-0271_ref_003","unstructured":"H. Jang, S. Kim and T. Lam, Kaggle competitions: author identification and Statoil\/C-CORE Iceberg Classifier Challenge, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA, Datamining B565 Fall 2017."},{"key":"2025120523362752966_j_jisys-2018-0271_ref_004","unstructured":"L. Jun, P. Atharva and S. Dhruv, Iceberg-ship classifier using SAR image maps, Final Project Report 2017, Stanford University."},{"key":"2025120523362752966_j_jisys-2018-0271_ref_005","unstructured":"D.-H. Lee, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, in: ICML 2013 Workshop: Challenges in Representation Learning (WREPL), Atlanta, GA, USA, 2013."},{"key":"2025120523362752966_j_jisys-2018-0271_ref_006","doi-asserted-by":"crossref","unstructured":"J. Tang, C. Deng, G.-B. Huang and B. Zhao, Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine, IEEE Trans. Geosci. Remote Sens. 53 (2015), 1174\u20131185.","DOI":"10.1109\/TGRS.2014.2335751"},{"key":"2025120523362752966_j_jisys-2018-0271_ref_007","unstructured":"P. Velickovic, Deep learning for complete beginners: convolutional neural networks with keras."},{"key":"2025120523362752966_j_jisys-2018-0271_ref_008","doi-asserted-by":"crossref","unstructured":"G. Wu, J. de Leeuw, A. K. Skidmore, Y. Liu and H. H. T. Prins, Performance of Landsat TM in ship detection in turbid waters, Int. J. Appl. Earth Obs. Geoinf. 11 (2009), 54\u201361.","DOI":"10.1016\/j.jag.2008.07.001"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/jisys\/29\/1\/article-p1514.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0271\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:37:39Z","timestamp":1764977859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0271\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,24]]},"references-count":8,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,4,25]]},"published-print":{"date-parts":[[2019,12,18]]}},"alternative-id":["10.1515\/jisys-2018-0271"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2018-0271","relation":{},"ISSN":["2191-026X","0334-1860"],"issn-type":[{"type":"electronic","value":"2191-026X"},{"type":"print","value":"0334-1860"}],"subject":[],"published":{"date-parts":[[2019,7,24]]}}}