{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:19:52Z","timestamp":1780078792052,"version":"3.54.0"},"reference-count":20,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fund of the National Natural Science Foundation of China","award":["61801142"],"award-info":[{"award-number":["61801142"]}]},{"name":"Fund of the National Natural Science Foundation of China","award":["61601135"],"award-info":[{"award-number":["61601135"]}]},{"name":"Fund of the National Natural Science Foundation of China","award":["61675051"],"award-info":[{"award-number":["61675051"]}]},{"name":"Natural Science Foundation of Heilongjiang Province of China","award":["QC201706802"],"award-info":[{"award-number":["QC201706802"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy.<\/jats:p>","DOI":"10.3390\/ijgi8060276","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T11:15:58Z","timestamp":1560424558000},"page":"276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0751-7726","authenticated-orcid":false,"given":"Yiming","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2354-0141","authenticated-orcid":false,"given":"Nan","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images","volume":"99","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Li, Q., Mou, L., Liu, Q., Wang, Y., and Zhu, X.X. (2018). HSF-Net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery. IEEE Trans. Geosci. Remote Sens., 1\u201315.","DOI":"10.1109\/TGRS.2018.2829166"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201312). Fast R-CNN. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015, January 7\u201312). you only look once: Unified, real-time object detection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Fu, C., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, J., Weng, L., and Yang, Y. (2017, January 17\u201320). Rotated region based CNN for ship detection. In Proceeding of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296411"},{"key":"ref_11","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic ship detection of remote sensing images from Google Earth in complex scenes based on multi-scale rotation dense feature pyramid networks. Remote Sens., 10."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (2018, January 18\u201321). Improving deep learning with generic data augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1093\/mnras\/stv632","article-title":"Rotation-invariant convolutional neural networks for galaxy morphology prediction","volume":"450","author":"Dieleman","year":"2015","journal-title":"Monthly Not. R. Astron. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17687","DOI":"10.1038\/s41598-018-36047-2","article-title":"A perlin noise-based augmentation strategy for deep learning with small data samples of HRCT images","volume":"8","author":"Bae","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fawzi, A., Samulowitz, H., Turaga, D., and Frossard, P. (2016, January 25\u201328). Adaptive data augmentation for image classification. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533048"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, Y.M., Qin, X.L., and Zhang, J. (2018, January 27\u201329). Low effectiveness of non-geometric-operation data augmentations for lesion segmentation with fully convolution networks. Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.","DOI":"10.1109\/ICIVC.2018.8492891"},{"key":"ref_17","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15713","DOI":"10.1109\/ACCESS.2018.2815741","article-title":"Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks","volume":"6","author":"Tang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28894","DOI":"10.1109\/ACCESS.2019.2902121","article-title":"Data augmentation for X-ray prohibited item images using generative adversarial networks","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/6\/276\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:58:12Z","timestamp":1760187492000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/6\/276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":20,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["ijgi8060276"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8060276","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,13]]}}}