{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:58:13Z","timestamp":1761562693488,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFD0701000"],"award-info":[{"award-number":["2017YFD0701000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51979275"],"award-info":[{"award-number":["51979275"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)\/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model; meanwhile, the density of the crop point cloud was quadrupled.<\/jats:p>","DOI":"10.3390\/rs12193208","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"3208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["UAV-Borne LiDAR Crop Point Cloud Enhancement Using Grasshopper Optimization and Point Cloud Up-Sampling Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9030-618X","authenticated-orcid":false,"given":"Jian","family":"Chen","sequence":"first","affiliation":[{"name":"College of Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0065-7374","authenticated-orcid":false,"given":"Zichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4932-5338","authenticated-orcid":false,"given":"Shubo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6711-530X","authenticated-orcid":false,"given":"Yu","family":"Han","sequence":"additional","affiliation":[{"name":"College of Water Resources &amp; Civil Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"060901","DOI":"10.1117\/1.OE.51.6.060901","article-title":"Review of LiDAR: A historic, yet emerging, sensor technology with rich phenomenology","volume":"51","author":"McManamon","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez-Arellano, M., Griepentrog, H.W., Reiser, D., and Paraforos, D.S. (2016). 3-D Imaging Systems for Agricultural Applications\u2014A Review. Sensors, 16.","DOI":"10.3390\/s16050618"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.isprsjprs.2010.08.006","article-title":"Measuring tree stem diameters using intensity profiles from ground-based scanning LiDAR from a fixed viewpoint","volume":"66","author":"Lovell","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"426","DOI":"10.5589\/m08-046","article-title":"Retrieval of forest structural parameters using a ground-based LiDAR instrument (Echidna)","volume":"34","author":"Strahler","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TGRS.2012.2205003","article-title":"Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning","volume":"51","author":"Zheng","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"Application status and development of point cloud density characteristics of airborne LiDAR","volume":"16","author":"Lai","year":"2018","journal-title":"Geospat. Inf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Quan, W., Gong, X., Fang, J., and Li, J. (2015). Prospects of INS\/CNS\/GNSS Integrated Navigation Technology. INS\/CNS\/GNSS Integrated Navigation Technology, Springer.","DOI":"10.1007\/978-3-662-45159-5"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fu, B., Liu, J., and Wang, Q. (2019, January 4\u20139). Multi-sensor integrated navigation system for ships based on adaptive Kalman filter. Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China.","DOI":"10.1109\/ICMA.2019.8816392"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"23286","DOI":"10.3390\/s150923286","article-title":"INS\/GPS\/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm","volume":"15","author":"Gao","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s11071-014-1852-9","article-title":"Asynchronous direct Kalman filtering approach for underwater integrated navigation system","volume":"80","author":"Shabani","year":"2015","journal-title":"Nonlinear Dyn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.ast.2018.07.026","article-title":"Intelligent GNSS\/INS integrated navigation system for a commercial UAV flight control system","volume":"80","author":"Zhang","year":"2018","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, H., Han, H., Wang, J., Xiao, H., and Wang, C. (2020). Single-frequency GPS\/BDS RTK and INS ambiguity resolution and positioning performance enhanced with positional polynomial fitting constraint. Remote Sens., 12.","DOI":"10.3390\/rs12152374"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Norouz, M., Ebrahimi, M., and Arbabmir, M. (2017, January 2\u20134). Modified Unscented Kalman Filter for improving the integrated navigation system. Proceedings of the 25th Iranian Conference on Electrical Engineering, Tehran, Iran.","DOI":"10.1109\/IranianCEE.2017.7985139"},{"key":"ref_14","unstructured":"Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learing, Addison-Wesley."},{"key":"ref_15","unstructured":"Eberhart, R., and Kennedy, J. (1995, January 4\u20136). A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan."},{"key":"ref_16","unstructured":"Colorni, A., Dorigo, M., and Maniezzo, V. (1991, January 11\u201313). Distributed optimization by ant colonies. Proceedings of the 1st European Conference on Artificial Life, Paris, France."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, X. (2010). A new metaheuristic bat-inspired algorithm. Proceedings of Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","article-title":"Grasshopper optimisation algorithm: Theory and application","volume":"105","author":"Saremi","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3894987","DOI":"10.1155\/2020\/3894987","article-title":"An enhanced grasshopper optimization algorithm to the Bin packing problem","volume":"2020","author":"Feng","year":"2020","journal-title":"J. Control Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107624","DOI":"10.1016\/j.patcog.2020.107624","article-title":"BLOCK-DBSCAN: Fast clustering for large scale data","volume":"109","author":"Chen","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.measurement.2015.07.015","article-title":"Methods of laser scanning point clouds integration in precise 3D building modelling","volume":"74","author":"Kedzierski","year":"2015","journal-title":"Measurement"},{"key":"ref_22","first-page":"587","article-title":"Point density evaluation of airborne LiDAR datasets","volume":"21","author":"Rupink","year":"2015","journal-title":"J. Univers. Comput. Sci."},{"key":"ref_23","first-page":"1","article-title":"Edge-aware point set resampling","volume":"32","author":"Huang","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.knosys.2017.12.037","article-title":"Evolutionary population dynamics and grasshopper optimization approach for feature selection problems","volume":"145","author":"Mafarja","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., and Huszar, F. (2017, January 21\u201326). Photo-realistic single image super-resolution using generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yu, L., Li, X., and Fu, C.W. (2018, January 19\u201321). PU-net: Point cloud upsampling network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00295"},{"key":"ref_27","first-page":"1006","article-title":"Information extraction of ecological canal system based on remote sensing data of unmanned aerial vehicle","volume":"36","author":"Zhang","year":"2018","journal-title":"J. Drain. Irrig. Mach. Eng."},{"key":"ref_28","first-page":"1137","article-title":"Weed classification of remote sensing ecological irrigation area by UAV based on deep learning","volume":"11","author":"Wang","year":"2018","journal-title":"J. Drain. Irrig. Mach. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:15:37Z","timestamp":1760177737000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":28,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193208"],"URL":"https:\/\/doi.org\/10.3390\/rs12193208","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,10,1]]}}}