{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:29:38Z","timestamp":1761629378956,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models\u2019 classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models\u2019 overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.<\/jats:p>","DOI":"10.3390\/s21061994","type":"journal-article","created":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T23:52:06Z","timestamp":1615765926000},"page":"1994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images"],"prefix":"10.3390","volume":"21","author":[{"given":"Qian","family":"Ma","sequence":"first","affiliation":[{"name":"Institute of Soil and Water Conservation, Chinese Academy of Sciences, Ministry of Water Resources, Yangling 712100, China"},{"name":"College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Soil and Water Conservation, Chinese Academy of Sciences, Ministry of Water Resources, Yangling 712100, China"},{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenjin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2718-4343","authenticated-orcid":false,"given":"Shide","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haipeng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1126\/science.1257469","article-title":"World population stabilization unlikely this century","volume":"346","author":"Gerland","year":"2014","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite image time series analysis under time warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. 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