{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T08:54:10Z","timestamp":1774428850840,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,3]],"date-time":"2018-09-03T00:00:00Z","timestamp":1535932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0501501"],"award-info":[{"award-number":["2016YFB0501501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41331176"],"award-info":[{"award-number":["41331176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.<\/jats:p>","DOI":"10.3390\/s18092929","type":"journal-article","created":{"date-parts":[[2018,9,3]],"date-time":"2018-09-03T10:50:51Z","timestamp":1535971851000},"page":"2929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7700-7284","authenticated-orcid":false,"given":"Yuanyuan","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-8148","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/LGRS.2013.2262073","article-title":"Ship classification in TerraSAR-X images with feature space based sparse representation","volume":"10","author":"Xing","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"698370","DOI":"10.1155\/2013\/698370","article-title":"Ship classification with high resolution TerraSAR-X imagery based on analytic hierarchy process","volume":"2013","author":"Zhao","year":"2013","journal-title":"Int. J. Antennas Propag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/LGRS.2013.2268875","article-title":"A novel hierarchical ship classifier for COSMO-SkyMed sar data","volume":"11","author":"Wang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ji, K., Xing, X., Chen, W., Zou, H., and Chen, J. (2013, January 21\u201326). Ship classification in TerraSAR-X SAR images based on classifier combination. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723352"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.1109\/JSTARS.2017.2665346","article-title":"Learning attribute representations for remote sensing ship category classification","volume":"10","author":"Oliveau","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"975","article-title":"Vessel classification in cosmo-skymed SAR data using hierarchical feature selection","volume":"Volume 47","author":"Schreier","year":"2015","journal-title":"Proceedings of the 36th International Symposium on Remote Sensing of Environment"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1049\/el.2016.4598","article-title":"2D comb feature for analysis of ship classification in high-resolution SAR imagery","volume":"53","author":"Leng","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2015.2506570","article-title":"Ship classification in SAR image by joint feature and classifier selection","volume":"13","author":"Lang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/LGRS.2017.2734889","article-title":"Ship classification in moderate-resolution SAR image by naive geometric features-combined multiple kernel learning","volume":"14","author":"Lang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/LGRS.2016.2514482","article-title":"Ship classification based on superstructure scattering features in SAR images","volume":"13","author":"Jiang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Knapskog, A.O., Brovoll, S., and Torvik, B. (2010, January 10\u201314). Characteristics of ships in harbour investigated in simultaneous images from TerraSAR-X and PicoSAR. Proceedings of the 2010 IEEE Radar Conference, Arlington, Virginia, USA.","DOI":"10.1109\/RADAR.2010.5494583"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/TGRS.2004.834654","article-title":"On the use of permanent symmetric scatterers for ship characterization","volume":"42","author":"Touzi","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/TGRS.2008.2008721","article-title":"Exploitation of ship scattering in polarimetric SAR for an improved classification under high clutter conditions","volume":"47","author":"Margarit","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_15","unstructured":"Russell, S.J., Norvig, P., Canny, J.F., Malik, J.M., and Edwards, D.D. (2003). Artificial Intelligence: A Modern Approach, Prentice Hall."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/JOE.2017.2767106","article-title":"Ship classification in TerraSAR-X images with convolutional neural networks","volume":"43","author":"Bentes","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_22","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_23","unstructured":"(2018, March 30). Imagenet. Available online: http:\/\/www.image-net.org\/."},{"key":"ref_24","unstructured":"(2018, March 30). Common Objects in Context. Available online: http:\/\/cocodataset.org\/#home."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_26","unstructured":"Henderson, F.M., Ryerson, R.A., Lewis, A.J., Photogrammetry, A.S.F., and Sensing, R. (1998). Principles and Applications of Imaging Radar, Wiley."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1126\/science.1192788","article-title":"How to grow a mind: Statistics, structure, and abstraction","volume":"331","author":"Tenenbaum","year":"2011","journal-title":"Science"},{"key":"ref_29","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_31","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_32","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Palais des Congr\u00e8s de Montr\u00e9al, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., and Zhang, H. (2017, January 13\u201314). Combining single shot multibox detector with transfer learning for ship detection using sentinel-1 images. Proceedings of the 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China.","DOI":"10.1109\/BIGSARDATA.2017.8124924"},{"key":"ref_34","unstructured":"(2018, March 30). Keras: The Python Deep Learning Library. Available online: https:\/\/keras.io\/."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Orr, G.B., and M\u00fcller, K.-R. (1998). Efficient backprop. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/3-540-49430-8"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Netw."},{"key":"ref_37","unstructured":"Hinton, G., Srivastava, N., and Swersky, K. (2018, August 27). Rmsprop: Divide the Gradient by a Running Average of Its Recent Magnitude. Available online: https:\/\/www.coursera.org\/lecture\/neural-networks\/rmsprop-divide-the-gradient-by-a-running-average-of-its-recent-magnitude-YQHki."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Wei, L., Yangqing, J., 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_39","doi-asserted-by":"crossref","unstructured":"Webb, A.R. (2003). Statistical Pattern Recognition, John Wiley & Sons.","DOI":"10.1002\/0470854774"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fdez, I., Canosa, A., Mucientes, M., and Bugar\u00edn, A. (2015, January 2\u20135). Stac: A web platform for the comparison of algorithms using statistical tests. Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey.","DOI":"10.1109\/FUZZ-IEEE.2015.7337889"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2015). Ensemble Learning, Springer.","DOI":"10.1007\/978-1-4899-7488-4_293"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/2929\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:18:38Z","timestamp":1760195918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/2929"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,3]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["s18092929"],"URL":"https:\/\/doi.org\/10.3390\/s18092929","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,3]]}}}