{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T22:19:16Z","timestamp":1774909156151,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"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":["61601006"],"award-info":[{"award-number":["61601006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4192021"],"award-info":[{"award-number":["4192021"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Equipment Pre-Research Foundation","award":["61404130312"],"award-info":[{"award-number":["61404130312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ship detection plays a significant role in military and civil fields. Although some state-of-the-art detection methods, based on convolutional neural networks (CNN) have certain advantages, they still cannot solve the challenge well, including the large size of images, complex scene structure, a large amount of false alarm interference, and inshore ships. This paper proposes a ship detection method from optical remote sensing images, based on visual attention enhanced network. To effectively reduce false alarm in non-ship area and improve the detection efficiency from remote sensing images, we developed a light-weight local candidate scene network(     L 2     CSN) to extract the local candidate scenes with ships. Then, for the selected local candidate scenes, we propose a ship detection method, based on the visual attention DSOD(VA-DSOD). Here, to enhance the detection performance and positioning accuracy of inshore ships, we both extract semantic features, based on DSOD and embed a visual attention enhanced network in DSOD to extract the visual features. We test the detection method on a large number of typical remote sensing datasets, which consist of Google Earth images and GaoFen-2 images. We regard the state-of-the-art method [sliding window DSOD (SW+DSOD)] as a baseline, which achieves the average precision (AP) of 82.33%. The AP of the proposed method increases by 7.53%. The detection and location performance of our proposed method outperforms the baseline in complex remote sensing scenes.<\/jats:p>","DOI":"10.3390\/s19102271","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T11:21:22Z","timestamp":1558005682000},"page":"2271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Ship Detection for Optical Remote Sensing Images Based on Visual Attention Enhanced Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Fukun","family":"Bi","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyuan","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"74907","DOI":"10.1109\/JSTARS.2013.2273393","article-title":"Validating a Notch Filter for Detection of Targets at Sea with ALOS-PALSAR Data: Tokyo Bay","volume":"7","author":"Marino","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.isprsjprs.2008.01.005","article-title":"A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data","volume":"63","author":"Durieux","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5198","DOI":"10.1109\/TGRS.2017.2703621","article-title":"Multiple moving object detection from UAV videos using trajectories of matched regional adjacency graphs","volume":"55","author":"Kalantar","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3892","DOI":"10.1109\/JSTARS.2014.2319195","article-title":"AIS-Based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance","volume":"8","author":"Pelich","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.landurbplan.2006.02.014","article-title":"Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data","volume":"79","author":"Martinuzzi","year":"2007","journal-title":"Lands. Urban Plann."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TGRS.2014.2335751","article-title":"Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine","volume":"53","author":"Tang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/LGRS.2013.2272492","article-title":"A new method on inshore ship detection in highresolution satellite images using shape and context information","volume":"11","author":"Liu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/LGRS.2013.2273552","article-title":"Ship detection from optical satellite images based on sea surface analysis","volume":"11","author":"Yang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/LGRS.2009.2031826","article-title":"Characterization of a Bayesian ship detection method in optical satellite images","volume":"7","author":"Proia","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TGRS.2013.2282820","article-title":"An improved iterative censoring scheme for CFAR ship detection with SAR imagery","volume":"52","author":"An","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dong, C., Liu, J., and Xu, F. (2018). Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor. Remote Sens., 18.","DOI":"10.3390\/rs10030400"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, J.H., Sun, M.C., Zeng, D.D., and Wang, X.A. (2017). Hierarchical Maritime Object Detection Method for Optical Remote Sensing Imagery. Remote sens., 9.","DOI":"10.3390\/rs9030280"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2011.2180695","article-title":"A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images","volume":"9","author":"Bi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bi, F.K., Chen, J., Zhuang, Y., Bian, M.M., and Zhang, Q.J. (2017). A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images. Sensors, 17.","DOI":"10.3390\/s17071470"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (\u20131, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_17","unstructured":"(2019, May 15). SSD: Single shot multibox detector. Available online: https:\/\/www.cs.unc.edu\/~wliu\/papers\/ssd.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 8\u201316). Deep Residual Learning for Image Recognition. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/LGRS.2017.2751559","article-title":"Locality adaptive discriminant analysis for spectral\u2013spatial classification of hyperspectral images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1109\/TCYB.2016.2572306","article-title":"Automatic subspace learning via principal coefficients embedding","volume":"47","author":"Peng","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.isprsjprs.2018.05.005","article-title":"A light and faster regional convolutional neural network for object detection in optical remote sensing images","volume":"141","author":"Ding","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TITS.2017.2749964","article-title":"Embedding structured contour and location prior in siamesed fully convolutional networks for road detection","volume":"19","author":"Wang","year":"2018","journal-title":"IEEE Trans. Intell. Transp."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1038\/srep03188","article-title":"Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data","volume":"3","author":"Dutta","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1080\/2150704X.2015.1072288","article-title":"Object recognition in remote sensing images using sparse deep belief networks","volume":"6","author":"Diao","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gao, F., Yang, Y., Wang, J., Sun, J.P., Yang, E.F., and Zhou, H.Y. (2018). A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images. Remote Sens., 10.","DOI":"10.3390\/rs10060846"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5653","DOI":"10.1109\/TGRS.2017.2711275","article-title":"Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"568","DOI":"10.3390\/rs10040568","article-title":"A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification","volume":"10","author":"Zhu","year":"2018","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2018.2856921","article-title":"Toward Arbitrary-Oriented Ship Detection with Rotated Region Proposal and Discrimination Networks","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/LGRS.2018.2813094","article-title":"Arbitrary-Oriented Ship Detection Framework in Optical Remote-Sensing Images","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7147","DOI":"10.1109\/TGRS.2018.2848901","article-title":"HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., and Lin, Z. (2017). Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection. Remote Sens., 9.","DOI":"10.3390\/rs9080860"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5832","DOI":"10.1109\/TGRS.2016.2572736","article-title":"Ship Detection in Spaceborne Optical Image with SVD Networks","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Loffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shen, Z., Liu, Z., Li, J., Jiang, Y.-G., Chen, Y., and Xue, X. (2017, January 22\u201329). Dsod: Learning deeply supervised object detectors from scratch. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.212"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3676","DOI":"10.1109\/TIP.2018.2825107","article-title":"TextBoxes++: A Single-Shot Oriented Scene Text Detector","volume":"27","author":"Liao","year":"2017","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:52:31Z","timestamp":1760187151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,16]]},"references-count":39,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102271"],"URL":"https:\/\/doi.org\/10.3390\/s19102271","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,16]]}}}