{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:58:55Z","timestamp":1768417135298,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"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":["42071445"],"award-info":[{"award-number":["42071445"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS202118"],"award-info":[{"award-number":["OFSLRSS202118"]}]},{"name":"Beijing Outstanding Young Scientist Program","award":["BJJWZYJH01201910028032"],"award-info":[{"award-number":["BJJWZYJH01201910028032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.<\/jats:p>","DOI":"10.3390\/rs13224497","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T21:39:07Z","timestamp":1636493947000},"page":"4497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene"],"prefix":"10.3390","volume":"13","author":[{"given":"Jianjun","family":"Zou","sequence":"first","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Zhenxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8118-3889","authenticated-orcid":false,"given":"Dong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Qinghua","family":"Li","sequence":"additional","affiliation":[{"name":"Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60201, USA"}]},{"given":"Lan","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Ruofei","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4175-7590","authenticated-orcid":false,"given":"Liqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Jinghan","family":"Sha","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China"},{"name":"Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"key":"ref_1","unstructured":"Jelalian, A.V. (1992). Laser Radar Systems, Artech House."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103903","DOI":"10.1016\/j.landurbplan.2020.103903","article-title":"Point cloud modeling as a bridge between landscape design and planning","volume":"203","author":"Urech","year":"2020","journal-title":"Landsc. Urban Plan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). In Multi-view 3d object detection network for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2016.10.022","article-title":"A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory","volume":"194","author":"Nilsson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprs-archives-XLII-5-W2-9-2019","article-title":"Comparison of software for airborne laser scanning data processing in smart city applications","volume":"XLII-5\/W2","author":"Badenko","year":"2019","journal-title":"Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TRO.2013.2279412","article-title":"3-D mapping with an RGB-D camera","volume":"30","author":"Endres","year":"2014","journal-title":"IEEE Trans. Robot."},{"key":"ref_7","first-page":"1633","article-title":"Estimating position of mobile robots from omnidirectional vision using an adaptive algorithm","volume":"45","author":"Li","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TCYB.2015.2430526","article-title":"Robotic online path planning on point cloud","volume":"46","author":"Liu","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_9","first-page":"37","article-title":"3D building model reconstruction from point clouds and ground plans","volume":"XXXIV-3\/W4","author":"Vosselman","year":"2001","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci."},{"key":"ref_10","unstructured":"Wang, F., Zhuang, Y., Zhang, H., and Gu, H. (2020). Real-time 3-D semantic scene parsing with LiDAR sensors. IEEE Trans. Cybern., 1\u201313."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2013.11.015","article-title":"Automatic registration of optical imagery with 3D LiDAR data using statistical similarity","volume":"88","author":"Parmehr","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/LGRS.2018.2872353","article-title":"Multiscale sparse features embedded 4-points congruent sets for global registration of TLS point clouds","volume":"16","author":"Xu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"Method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2007.05.012","article-title":"A method for automated registration of unorganised point clouds","volume":"63","author":"Bae","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.isprsjprs.2013.02.019","article-title":"Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge","volume":"79","author":"Gressin","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360684","article-title":"4-points congruent sets for robust pairwise surface registration","volume":"27","author":"Aiger","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2016, January 8\u201316). Fast global registration. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_47"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., and Beetz, M. (2008, January 22\u201326). Aligning point cloud views using persistent feature histograms. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/cgf.12446","article-title":"Super 4pcs fast global pointcloud registration via smart indexing","volume":"33","author":"Mellado","year":"2014","journal-title":"Comput. Graph. Forum"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1007\/s001380050048","article-title":"Estimating 3-D rigid body transformations: A comparison of four major algorithms","volume":"9","author":"Eggert","year":"1997","journal-title":"Mach. Vis. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast point feature histograms (FPFH) for 3D registration. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cheng, L., Chen, S., Liu, X., Xu, H., Wu, Y., Li, M., and Chen, Y. (2018). Registration of laser scanning point clouds: A review. Sensors, 18.","DOI":"10.3390\/s18051641"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.isprsjprs.2020.01.020","article-title":"Object-based incremental registration of terrestrial point clouds in an urban environment","volume":"161","author":"Ge","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. (2015, January 7\u201312). 3d shapenets: A deep representation for volumetric shapes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ku, J., Mozifian, M., Lee, J., Harakeh, A., and Waslander, S.L. (2018, January 1\u20135). Joint 3d proposal generation and object detection from view aggregation. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594049"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, L., Zhu, S., Fu, H., Tan, P., and Tai, C.L. (2020, January 14\u201319). End-to-end learning local multi-view descriptors for 3D point clouds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR42600.2020.00199"},{"key":"ref_27","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, J., and Lee, G.H. (2019, January 27\u201328). Usip: Unsupervised stable interest point detection from 3d point clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00045"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yew, Z.J., and Lee, G.H. (2018, January 8\u201314). 3dfeat-net: Weakly supervised local 3d features for point cloud registration. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_37"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., and Ilic, S. (2018, January 18\u201322). Ppfnet: Global context aware local features for robust 3d point matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00028"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., and Ilic, S. (2018, January 8\u201314). Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_37"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yew, Z.J., and Lee, G.H. (2020, January 14\u201319). Rpm-net: Robust point matching using learned features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01184"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Birdal, T., Deng, H., and Tombari, F. (2019, January 16\u201320). 3D point capsule networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00110"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Birdal, T., and Ilic, S. (2015, January 19\u201322). Point pair features based object detection and pose estimation revisited. Proceedings of the 2015 International Conference on 3D Vision (3DV), Lyon, France.","DOI":"10.1109\/3DV.2015.65"},{"key":"ref_35","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, G., Muller, M., Thabet, A., and Ghanem, B. (2019, January 27\u201328). Deepgcns: Can gcns go as deep as cnns?. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref_37","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_38","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4\u20139). Dynamic routing between capsules. Proceedings of the 31st Annual Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/34.765655","article-title":"Using spin images for efficient object recognition in cluttered 3D scenes","volume":"21","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/34.993558","article-title":"Shape matching and object recognition using shape contexts","volume":"24","author":"Belongie","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Frome, A., Huber, D., Kolluri, R., B\u00fclow, T., and Malik, J. (2004, January 11\u201314). Recognizing objects in range data using regional point descriptors. Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24672-5_18"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Di Stefano, L. (2010, January 25). Unique shape context for 3D data description. Proceedings of the ACM Workshop on 3D Object Retrieval, Firenze, Italy.","DOI":"10.1145\/1877808.1877821"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Guo, Y., Sohel, F.A., Bennamoun, M., Wan, J., and Lu, M. (2013, January 12\u201314). RoPS: A local feature descriptor for 3D rigid objects based on rotational projection statistics. Proceedings of the 2013 1st International Conference on Communications, Signal Processing, and Their Applications (ICCSPA), Sharjah, United Arab Emirates.","DOI":"10.1109\/ICCSPA.2013.6487310"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cviu.2014.04.011","article-title":"SHOT: Unique signatures of histograms for surface and texture description","volume":"125","author":"Salti","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_45","unstructured":"Steder, B., Rusu, R.B., Konolige, K., and Burgard, W. (2010, January 18\u201322). NARF: 3D range image features for object recognition. Proceedings of the Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics at the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Nie\u00dfner, M., Fisher, M., Xiao, J., and Funkhouser, T. (2017, January 21\u201326). 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.29"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/LGRS.2019.2910546","article-title":"3-D deep feature construction for mobile laser scanning point cloud registration","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lu, W., Wan, G., Zhou, Y., Fu, X., Yuan, P., and Song, S. (2019, January 27\u201328). Deepvcp: An end-to-end deep neural network for point cloud registration. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00010"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Choy, C., Park, J., and Koltun, V. (2019, January 27\u201328). Fully convolutional geometric features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00905"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gojcic, Z., Zhou, C., Wegner, J.D., and Wieser, A. (2019, January 16\u201320). The perfect match: 3d point cloud matching with smoothed densities. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00569"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bai, X., Luo, Z., Zhou, L., Fu, H., Quan, L., and Tai, C.L. (2020, January 14\u201319). D3Feat: Joint learning of dense detection and description of 3D local features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00639"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Guibas, L.J. (2019, January 27\u201328). Kpconv: Flexible and deformable convolution for point clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Khoury, M., Zhou, Q.-Y., and Koltun, V. (2017, January 22\u201329). Learning compact geometric features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.26"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.inffus.2020.03.008","article-title":"Learning to fuse local geometric features for 3D rigid data matching","volume":"61","author":"Yang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1177\/0278364912458814","article-title":"Challenging data sets for point cloud registration algorithms","volume":"31","author":"Pomerleau","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ma, Y., Guo, Y., Zhao, J., Lu, M., Zhang, J., and Wan, J. (2016, January 27\u201330). Fast and accurate registration of structured point clouds with small overlaps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.86"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:28:10Z","timestamp":1760167690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,9]]},"references-count":57,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224497"],"URL":"https:\/\/doi.org\/10.3390\/rs13224497","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,9]]}}}