{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:09:19Z","timestamp":1774937359190,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T00:00:00Z","timestamp":1553817600000},"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":["2016YFB0501300 and 2016YFB0501302"],"award-info":[{"award-number":["2016YFB0501300 and 2016YFB0501302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771031, 61501009 and 61371134"],"award-info":[{"award-number":["61771031, 61501009 and 61371134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YWF-18-BJ-J-231"],"award-info":[{"award-number":["YWF-18-BJ-J-231"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Micro-nano satellites have provided a large amount of remote sensing images for many earth observation applications. However, the hysteresis of satellite-ground mutual communication of massive remote sensing images and the low efficiency of traditional information processing flow have become the bottlenecks for the further development of micro-nano satellites. To solve this problem, this paper proposes an on-board ship detection scheme based on deep learning and Commercial Off-The-Shelf (COTS) component, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. The on-board ship detection algorithm based on deep learning consists of a feature extraction network, Region Proposal Network (RPN) with square anchors, Global Average Pooling (GAP), and Bigger-Left Non-Maximum Suppression (BL-NMS). With the help of high performance COTS components, the proposed scheme can extract target patches and valuable information from remote sensing images quickly and accurately. A ground demonstration and verification system is built to verify the feasibility and effectiveness of our scheme. Our method achieves the performance with 95.9% recall and 80.5% precision in our dataset. Experimental results show that the scheme has a good application prospect in micro-nano satellites with limited power and computing resources.<\/jats:p>","DOI":"10.3390\/rs11070762","type":"journal-article","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T13:09:58Z","timestamp":1553864998000},"page":"762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component"],"prefix":"10.3390","volume":"11","author":[{"given":"Yuan","family":"Yao","sequence":"first","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beijing 100191, China"}]},{"given":"Zhiguo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1981-8307","authenticated-orcid":false,"given":"Haopeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beijing 100191, China"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"DFH Satellite Co., Ltd., Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Michael, Y., Lensky, I.M., Brenner, S., Tchetchik, A., Tessler, N., and Helman, D. (2018). Economic Assessment of Fire Damage to Urban Forest in the Wildland\u2014Urban Interface Using Planet Satellites Constellation Images. Remote Sens., 10.","DOI":"10.3390\/rs10091479"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jain, M., Srivastava, A.K., Joon, R.K., McDonald, A., Royal, K., Lisaius, M.C., and Lobell, D.B. (2016). Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sens., 8.","DOI":"10.3390\/rs8100860"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.12.033","article-title":"Vessel detection and classification from spaceborne optical images: A literature survey","volume":"207","author":"Kanjir","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Guerra, R., Barrios, Y., D\u00edaz, M., Santos, L., L\u00f3pez, S., and Sarmiento, R. (2018). A New Algorithm for the On-Board Compression of Hyperspectral Images. Remote Sens., 10.","DOI":"10.3390\/rs10030428"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, G., Zhang, R., Liu, N., Huang, J., and Zhou, X. (2017). On-Board Ortho-Rectification for Images Based on an FPGA. Remote Sens., 9.","DOI":"10.3390\/rs9090874"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, J., and Zhou, G. (2017). On-Board Detection and Matching of Feature Points. Remote Sens., 9.","DOI":"10.3390\/rs9060601"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Qi, B., Shi, H., Zhuang, Y., Chen, H., and Chen, L. (2018). On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery. Sensors, 18.","DOI":"10.3390\/s18051328"},{"key":"ref_8","first-page":"1","article-title":"Compression Algorithm Selection for Multispectral Mastcam Images","volume":"10","author":"Kwan","year":"2019","journal-title":"Signal Image Process. Int. J. (SIPIJ)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1109\/TGRS.2010.2046330","article-title":"A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features","volume":"48","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4511","DOI":"10.1109\/TGRS.2013.2282355","article-title":"Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature","volume":"52","author":"Shi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/LGRS.2017.2664118","article-title":"Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature","volume":"14","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","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_14","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Kwan, C., Chou, B., and Kwan, L.Y.M. (2018). A Comparative Study of Conventional and Deep Learning Target Tracking Algorithms for Low Quality Videos. International Symposium on Neural Networks, Springer.","DOI":"10.1007\/978-3-319-92537-0_60"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"042611","DOI":"10.1117\/1.JRS.11.042611","article-title":"Ship detection in optical remote sensing images based on deep convolutional neural networks","volume":"11","author":"Yao","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","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 Transa. Geosci. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., and Zhang, L. (2017). An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070666"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"50839","DOI":"10.1109\/ACCESS.2018.2869884","article-title":"Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/TIP.2017.2773199","article-title":"Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images","volume":"27","author":"Zou","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xia, G., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). 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.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/LGRS.2018.2829147","article-title":"Online Exemplar-Based Fully Convolutional Network for Aircraft Detection in Remote Sensing Images","volume":"15","author":"Cai","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Q., Mou, L., Xu, Q., Zhang, Y., and Zhu, X.X. (2019). R-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos. IEEE Trans. Geosci. Remote Sens., 1\u201315.","DOI":"10.1109\/TGRS.2019.2895362"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Visualizing and Understanding Convolutional Networks. Computer Vision\u2013ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10602-1"},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_41","unstructured":"Lin, M., Chen, Q., and Yan, S. (arXiv, 2013). Network in network, arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yao, Y., Jiang, Z., and Zhang, H. (2016, January 10\u201315). High-resolution optical satellite image simulation of ship target in large sea scenes. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729314"},{"key":"ref_43","unstructured":"Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z. (arXiv, 2015). Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems, arXiv."},{"key":"ref_44","unstructured":"Redmon, J., and Farhadi, A. (arXiv, 2018). YOLOv3: An Incremental Improvement, arXiv."},{"key":"ref_45","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (arXiv, 2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:41:29Z","timestamp":1760186489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,29]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070762"],"URL":"https:\/\/doi.org\/10.3390\/rs11070762","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,29]]}}}