{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:03:04Z","timestamp":1780344184800,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research on Intelligent Evaluation Method of Equipment Capability Based on Key Elements of Index","award":["61977059"],"award-info":[{"award-number":["61977059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.<\/jats:p>","DOI":"10.3390\/rs12203316","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion"],"prefix":"10.3390","volume":"12","author":[{"given":"Yulian","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihong","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengfa","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2017.2658950","article-title":"Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting","volume":"55","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Liu, S., Liang, J., Wang, C., Shi, J., and Zhang, X. (2020). HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12060989"},{"key":"ref_3","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., 10.","DOI":"10.3390\/rs10030400"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chang, Y.L., Anagaw, A., Chang, L., Wang, Y.C., Hsiao, C.Y., and Lee, W.H. (2019). Ship Detection Based on YOLOv2 for SAR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070786"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","unstructured":"Harel, J., Koch, C., and Perona, P. (2006, January 4\u20137). Graph-Based Visual Saliency. Proceedings of the 20th Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hou, X., and Zhang, L. (2007, January 17\u201322). Saliency Detection: A Spectral Residual Approach. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S.S., Estrada, F.J., and Susstrunk, S. (2009, January 20\u201325). Frequency-tuned salient region detection. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2015.2511882","article-title":"A Novel Inshore Ship Detection via Ship Head Classification and Body Boundary Determination","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, J., Sun, M., Zeng, D., and Wang, X. (2017). A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9030280"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1109\/TGRS.2011.2112371","article-title":"Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic","volume":"49","author":"Margarit","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/LGRS.2015.2408355","article-title":"Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor from Optical Satellite Images","volume":"12","author":"Qi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A.C. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_15","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 23\u201328). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ju, M., Luo, H., Wang, Z., Hui, B., and Chang, Z. (2019). The application of improved YOLO V3 in multi-scale target detection. Appl. Sci., 9.","DOI":"10.3390\/app9183775"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ijleo.2019.02.038","article-title":"An improved tiny-yolov3 pedestrian detection algorithm","volume":"183","author":"Yi","year":"2019","journal-title":"Optik"},{"key":"ref_20","unstructured":"Lim, J., Astrid, M., Yoon, H., and Lee, S. (2019, January 15\u201321). Small Object Detection using Context and Attention. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., and Chao, D. (2020). Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Remote Sens., 12.","DOI":"10.20944\/preprints202003.0313.v2"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TCSVT.2019.2897980","article-title":"Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video","volume":"30","author":"Shao","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9325","DOI":"10.1109\/ACCESS.2020.2964540","article-title":"Attention Mask R-CNN for Ship Detection and Segmentation from Remote Sensing Images","volume":"8","author":"Nie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"1","article-title":"A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"An, Q., Pan, Z., and You, H. (2018). Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18020334"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"66816","DOI":"10.1109\/ACCESS.2018.2878733","article-title":"Physics Inspired Methods for Crowd Video Surveillance and Analysis: A Survey","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.physa.2019.04.033","article-title":"Crowd panic state detection using entropy of the distribution of enthalpy","volume":"525","author":"Zhang","year":"2019","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jpdc.2019.04.013","article-title":"Detection of ships in inland river using high-resolution optical satellite imagery based on mixture of deformable part models","volume":"132","author":"Song","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.patrec.2019.01.015","article-title":"Land contained sea area ship detection using spaceborne image","volume":"130","author":"Wang","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_32","unstructured":"He, K., Sun, J., and Tang, X. (2009, January 20\u201325). Single image haze removal using dark channel prior. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_35","unstructured":"(2020, January 01). Pycharm. Available online: http:\/\/www.jetbrains.com\/pycharm\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.neucom.2020.07.019","article-title":"Scene perception guided crowd anomaly detection","volume":"414","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","article-title":"Squeeze and excitation rank faster R-CNN for ship detection in SAR images","volume":"16","author":"Lin","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, R., Zhang, S., Wang, X., Wen, L., Shi, H., Bo, L., and Mei, T. (2019, January 15\u201321). ScratchDet: Training single-shot object detectors from scratch. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00237"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","article-title":"Vehicle detection in aerial imagery: A small target detection benchmark","volume":"34","author":"Razakarivony","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201322). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1109\/TGRS.2019.2900302","article-title":"Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:19:44Z","timestamp":1760177984000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":42,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12203316"],"URL":"https:\/\/doi.org\/10.3390\/rs12203316","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]}}}