{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:14:53Z","timestamp":1780488893898,"version":"3.54.1"},"reference-count":20,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,12]],"date-time":"2019-10-12T00:00:00Z","timestamp":1570838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Subproject of National Science and Technology Support Plan","award":["2017YFD0700401"],"award-info":[{"award-number":["2017YFD0700401"]}]},{"name":"Key Research and Development Projects in Zhejiang Province","award":["2017C02031"],"award-info":[{"award-number":["2017C02031"]}]},{"name":"Major Science and Technology Projects of Ningxia Hui Autonomous Region Key R&amp;D Program","award":["2017BY067"],"award-info":[{"award-number":["2017BY067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aimed at the problem of obstacle detection in farmland, the research proposed to adopt the method of farmland information acquisition based on unmanned aerial vehicle landmark image, and improved the method of extracting obstacle boundary based on standard correlation coefficient template matching and assessed the influence of different image resolutions on the precision of obstacle extraction. Analyzing the RGB image of farmland acquired by unmanned aerial vehicle remote sensing technology, this research got the following results. Firstly, we applied a method automatically registering coordinates, and the average deviations on the X and Y direction were 4.6 cm and 12.0 cm respectively, while the average deviations manually by ArcGIS were 4.6 cm and 5.7 cm. Secondly, with an improvement on the step of the traditional correlation coefficient template matching, we reduced the time of template matching from 12.2 s to 4.6 s. The average deviation between edge length of obstacles calculated by corner points extracted by the algorithm and that by actual measurement was 4.0 cm. Lastly, by compressing the original image on a different ratio, when the pixel reached 735 \u00d7 2174 (the image resolution reached 6 cm), the obstacle boundary was extracted based on correlation coefficient template matching, the average deviations of boundary points I of six obstacles on the X and Y were respectively 0.87 and 0.95 cm, and the whole process of detection took about 3.1 s. To sum up, it can be concluded that the algorithm of automatically registered coordinates and of automatically extracted obstacle boundary, which were designed in this research, can be applied to the establishment of a basic information collection system for navigation in future study. The best image pixel of obstacle boundary detection proposed after integrating the detection precision and detection time can be the theoretical basis for deciding the unmanned aerial vehicle remote sensing image resolution.<\/jats:p>","DOI":"10.3390\/s19204431","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T03:54:13Z","timestamp":1571025253000},"page":"4431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Research on Method of Farmland Obstacle Boundary Extraction in UAV Remote Sensing Images"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0277-677X","authenticated-orcid":false,"given":"Hui","family":"Fang","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture Rural Affairs, Shaanxi 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-6896","authenticated-orcid":false,"given":"Fei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,12]]},"reference":[{"key":"ref_1","first-page":"21","article-title":"Research progress of intelligent obstacle detection methods of vehicles and their application on agriculture","volume":"34","author":"He","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/TITS.2002.802934","article-title":"Fast obstacle detection for urban traffic situations","volume":"3","author":"Franke","year":"2002","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.optlaseng.2007.08.002","article-title":"A stereo vision-based obstacle detection system in vehicles","volume":"46","author":"Huh","year":"2008","journal-title":"Opt. Lasers Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1109\/TVT.2009.2039718","article-title":"Processing dense stereo data using elevation maps: Road surface, traffic isle, and obstacle detection","volume":"59","author":"Oniga","year":"2010","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","first-page":"88","article-title":"Study on edge detection method","volume":"42","author":"Wei","year":"2006","journal-title":"Comput. Eng. Appl."},{"key":"ref_6","unstructured":"Sun, J.B. (2009). Principle and Applications of Remote Sensing, Wuhan University Press."},{"key":"ref_7","unstructured":"He, Y., Ceng, H.Y., He, L.W., Liu, F., and Nie, P.C. (2017). Agricultural UAV Technology and Applications, Science Press."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compag.2004.02.006","article-title":"Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support","volume":"44","author":"Herwitz","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera","volume":"117","author":"Zarcotejada","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","first-page":"98","article-title":"Texture scale analysis and identification of seed maize fields based on UAV and satellite remote sensing images","volume":"33","author":"Zhang","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_11","first-page":"44","article-title":"Rape yield estimation research based on spectral analysis for UAV image","volume":"6","author":"Gong","year":"2017","journal-title":"J. Geomat."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00138-002-0070-5","article-title":"Fast template matching using bounded partial correlation","volume":"13","author":"Stefano","year":"2003","journal-title":"Mach. Vis. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.cviu.2008.11.010","article-title":"Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching","volume":"113","author":"Bhandarkar","year":"2009","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_14","first-page":"226","article-title":"Artificial Neural Network model for handwritten digit recognition based on template matching","volume":"44","author":"Zhe","year":"2008","journal-title":"Comput. Eng. Appl."},{"key":"ref_15","first-page":"73","article-title":"An hybrid template matching and threshold segmentation target localization method and application","volume":"42","author":"Cheng","year":"2018","journal-title":"Video Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.specom.2017.01.009","article-title":"Template-matching for text-dependent speaker verification","volume":"88","author":"Dey","year":"2017","journal-title":"Speech Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yin, W.X., Zhang, C., Zhu, H., Zhao, Y., and He, Y. (2017). Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180534"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","first-page":"28","article-title":"Improved pure pursuit algorithm for rice transplanter path tracking","volume":"49","author":"Li","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0168-1699(99)00061-7","article-title":"Agricultural automatic guidance research in North America","volume":"25","author":"Reid","year":"2000","journal-title":"Comput. Electron. Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/20\/4431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:25:49Z","timestamp":1760189149000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/20\/4431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,12]]},"references-count":20,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19204431"],"URL":"https:\/\/doi.org\/10.3390\/s19204431","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,12]]}}}