{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T11:16:50Z","timestamp":1768735010987,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"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":["61772387"],"award-info":[{"award-number":["61772387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071354"],"award-info":[{"award-number":["62071354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of Shaanxi Province","award":["2019ZDLGY03-03"],"award-info":[{"award-number":["2019ZDLGY03-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The on-board pose estimation of uncooperative target is an essential ability for close-proximity formation flying missions, on-orbit servicing, active debris removal and space exploration. However, the main issues of this research are: first, traditional pose determination algorithms result in a semantic gap and poor generalization abilities. Second, specific pose information cannot be accurately known in a complicated space target imaging environment. Deep learning methods can effectively solve these problems; thus, we propose a pose estimation algorithm that is based on deep learning. We use keypoints detection method to estimate the pose of space targets. For complicated space target imaging environment, we combined the high-resolution network with dilated convolution and online hard keypoint mining strategy. The improved network pays more attention to the obscured keypoints, has a larger receptive field, and improves the detection accuracy. Extensive experiments have been conducted and the results demonstrate that the proposed algorithms can effectively reduce the error rate of pose estimation and, compared with the related pose estimation methods, our proposed model has a higher detection accuracy and a lower pose determination error rate in the speed dataset.<\/jats:p>","DOI":"10.3390\/rs12233857","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T08:59:15Z","timestamp":1606294755000},"page":"3857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation"],"prefix":"10.3390","volume":"12","author":[{"given":"Junjie","family":"Xu","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8096-3370","authenticated-orcid":false,"given":"Bin","family":"Song","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Yang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoting","family":"Nan","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1504\/IJSPACESE.2013.059268","article-title":"PROBA-3 mission","volume":"1","author":"Castellani","year":"2013","journal-title":"Int. J. Space Sci. Eng."},{"key":"ref_2","unstructured":"A. F. R. Laboratory (2014, September 30). Fact sheet: Automated Navigation and Guidance Experiment for Local Space (ANGELS). Available online: http:\/\/www.kirtland.af.mil\/shared\/media\/document\/AFD-131204-039.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"834","DOI":"10.2514\/1.55638","article-title":"Spaceborne Autonomous Formation-Flying Experiment on the PRISMA Mission","volume":"35","author":"Ardaens","year":"2012","journal-title":"J. Guid. Control Dyn."},{"key":"ref_4","unstructured":"Pei, J., Walsh, M., Roithmayr, C., Karlgaard, C., and Murchison, L. (2017, January 20\u201324). Preliminary GN&C Design for the On-Orbit Autonomous Assembly of Nanosatellite Demonstration Mission. Proceedings of the AAS\/AIAA Astrodynamics Specialist Conference, Stevenson, WA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Reed, B.B., Smith, R.C., Naasz, B.J., Pellegrino, J.F., and Bacon, C.E. (2016, January 13\u201316). The Restore-L Servicing Mission. Proceedings of the AIAA Space Forum, Long Beach, CA, USA.","DOI":"10.2514\/6.2016-5478"},{"key":"ref_6","unstructured":"Bowen, J., Villa, M., and Williams, M. (2015, January 8\u201313). CubeSat based Rendezvous, Proximity Operations, and Docking in the CPOD Mission. Proceedings of the 29th Annual AIAA\/USU Small Satellite, Logan, UT, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100548","DOI":"10.1016\/j.paerosci.2019.05.008","article-title":"Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft","volume":"110","author":"Fonod","year":"2019","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_8","unstructured":"Cropp, A., and Palmer, P. (2002, January 14\u201318). Pose Estimation and Relative Orbit Determination of a Nearby Target Microsatellite using Passive Imagery. Proceedings of the 5th Cranfield Conference on Dynamics and Control of Systems and Structures in Space 2002, Cambridge, UK."},{"key":"ref_9","unstructured":"Kanani, K., Petit, A., Marchand, E., Chabot, T., and Gerber, B. (2012, January 1\u20135). Vision Based Navigation for Debris Removal Missions. Proceedings of the 63rd International Astronautical Congress, Naples, Italy."},{"key":"ref_10","unstructured":"D\u2019Amico, S., Benn, M., and Jorgensen, J. (2013, January 29\u201331). Pose Estimation of an Uncooperative Spacecraft. Proceedings of the 5th International Conference on Spacecraft Formation Flying Missions and Technologies, Munich, Germany."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2514\/1.A34124","article-title":"Robust Model-Based Monocular Pose Estimation for Noncooperative Spacecraft Rendezvous","volume":"55","author":"Sharma","year":"2018","journal-title":"J. Spacecr Rocket."},{"key":"ref_12","first-page":"10491","article-title":"Scale invariant feature transform","volume":"7","author":"Lindeberg","year":"2012","journal-title":"KTH Comput. Biol. CB"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Leonardis, A. (2006). SURF: Speeded Up Robust Features. Computer Vision\u2014ECCV 2006, Springer.","DOI":"10.1007\/11744047"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sharma, S., and Beierle, C. (2018, January 4\u201311). D\u2019Amico, S. Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks. Proceedings of the 2018 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2018.8396425"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Toshev, A., and Szegedy, C. (2014, January 24\u201327). DeepPose: Human pose estimation via deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kendall, A., Grimes, M., and Cipolla, R. (2015, January 7\u201312). PoseNet: A convolutional network for real-time 6-dof camera relocalization. Proceedings of the IEEE International Conference on Computer Vision, Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.336"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Su, H., Qi, C.R., Li, Y., and Guibas, L.J. (2015, January 7\u201312). Render for CNN: Viewpoint Estimation in Images Using CNNs Trained With Rendered 3D Model Views. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.308"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Schmidt, T., and Narayanan, V. (2017). PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes. arXiv.","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shi, J., Ulrich, S., and Ruel, S. (2018, January 8\u201312). CubeSat Simulation and Detection using Monocular Camera Images and Convolutional Neural Networks. Proceedings of the 2018 AIAA GUidance, Navigation, and Control Conference, Kissimmee, FL, USA.","DOI":"10.2514\/6.2018-1604"},{"key":"ref_20","unstructured":"Sharma, S., and D\u2019Amico, S. (2019, January 11\u201315). Pose estimation for noncooperative rendezvous using neural networks. Proceedings of the AIAA\/AAS Space Flight Mechanics Meeting, Portland, ME, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Proen\u00e7a, P.F., and Gao, Y. (August, January 31). Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197244"},{"key":"ref_22","unstructured":"Chen, B., Cao, J., Parra, A., and Chin, T. (November, January 27). Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_23","unstructured":"Huang, G., Chen, D., Li, T., Wu, F., van der Maaten, L., and Weinberger, K.Q. (2017). Multi-Scale Dense Networks for Resource Efficient Image Classification. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","article-title":"EPnP: An Accurate O(n) Solution to the PnP Problem","volume":"81","author":"Lepetit","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.paerosci.2017.07.001","article-title":"A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations","volume":"93","author":"Opromolla","year":"2017","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"898","DOI":"10.2514\/1.30734","article-title":"ENavigating the road to autonomous orbital rendezvous","volume":"44","author":"Woffinden","year":"2007","journal-title":"J. Spacecr. Rockets"},{"key":"ref_27","unstructured":"Buist, P., Teunissen, P., and Joosten, P. (2006, January 26\u201329). GNSS-guided relative positioning and attitude determination for missions with multiple spacecraft. Proceedings of the International Symposium GPS\/GNSS, Fort Worth, TX, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.2514\/1.28216","article-title":"Relative angles-only navigation and pose estimation for autonomous orbital rendezvous","volume":"30","author":"Woffinden","year":"2007","journal-title":"J. Guid. Control Dyn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1117\/1.1631921","article-title":"Review of 20 years of range sensor development","volume":"13","author":"Blais","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_30","first-page":"193","article-title":"Object identification in 3D flash lidar images","volume":"2","author":"Crosby","year":"2011","journal-title":"J. Pattern Recognit. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21485","DOI":"10.1364\/OE.19.021485","article-title":"Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor","volume":"19","author":"Kirmani","year":"2011","journal-title":"Opt. Express"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fasano, G., Grassi, M., and Accardo, D. (2009, January 6\u20139). A stereo-vision based system for autonomous navigation of an in-orbit servicing platform. Proceedings of the AIAA Infotech@Aero-space 2009, Seattle, WA, USA.","DOI":"10.2514\/6.2009-1934"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.actaastro.2015.12.032","article-title":"Comparative assessment of techniques for initial pose estimation using monocular vision","volume":"123","author":"Sharma","year":"2016","journal-title":"Acta Astronaut"},{"key":"ref_34","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the 26th Annual Conference on Neural Information Processing Systems, Red Hook, NY, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., and Vanhoucke, V. (2017, January 4\u201310). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_36","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) 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sharma, S., and D\u2019Amico, S. (2020). Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous. IEEE Trans Aerosp Electron Syst, 1.","DOI":"10.1109\/TAES.2020.2999148"},{"key":"ref_38","unstructured":"Cortes, C., and Lawrence, N.D. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems 28, Curran Associates, Inc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201320). Deep High-Resolution Representation Learning for Human Pose Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Harvard, A., Capuano, V., Shao, E., and Chung, S. (2020, January 6\u201310). Inception-v4, Spacecraft Pose Estimation from Monocular Images Using Neural Network Based Keypoints and Visibility Maps. Proceedings of the AIAA Scitech 2020 Forum, Orlando, FL, USA.","DOI":"10.2514\/6.2020-1874"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Labatut, P., Pons, J., and Keriven, R. (2007, January 14\u201321). Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4408892"},{"key":"ref_42","unstructured":"Leibe, B., and Matas, J. (2016). Stacked Hourglass Networks for Human Pose Estimation. Computer Vision\u2014ECCV 2016, Springer International Publishing."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wei, S., Ramakrishna, V., and Kanade, T. (2016, January 27\u201330). Convolutional Pose Machines. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.511"},{"key":"ref_44","first-page":"1","article-title":"Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors","volume":"58","author":"Diebel","year":"2006","journal-title":"Matrix"},{"key":"ref_45","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_46","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, Y., Hugonot, J., Fua, P., and Salzmann, M. (2019, January 15\u201321). Segmentation-Driven 6D Object Pose Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00350"},{"key":"ref_48","unstructured":"Sun, K., Zhao, Y., Jiang, B., and Cheng, T. (2019). High-Resolution Representations for Labeling Pixels and Regions. arXiv."},{"key":"ref_49","unstructured":"Navab, N., and Hornegger, J. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer International Publishing."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/S0031-3203(02)00262-5","article-title":"Feature fusion: Parallel strategy vs. serial strategy","volume":"36","author":"Yang","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_51","unstructured":"Ourselin, S., and Joskowicz, L. (2016). Regressing Heatmaps for Multiple Landmark Localization Using CNNs. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Springer International Publishing."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., and Huang, T.S. (2018, January 18\u201322). Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00759"},{"key":"ref_53","unstructured":"Yu, F., and Koltun, V. (2015). Multi-Scale Context Aggregation by Dilated Convolutions. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (2016, January 27\u201330). Training Region-Based Object Detectors With Online Hard Example Mining. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.1109\/TAES.2020.2989063","article-title":"Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results","volume":"56","author":"Kisantal","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_56","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 Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_57","unstructured":"Chen, K., Wang, J., and Pang, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:37:09Z","timestamp":1760179029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,25]]},"references-count":57,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233857"],"URL":"https:\/\/doi.org\/10.3390\/rs12233857","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,25]]}}}