Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images | |
Qian, Ma3,4; Wenting, Han2,4; Shenjin, Huang2; Shide, Dong1; Guang, Li2; Haipeng, Chen2 | |
刊名 | sensors |
2021-03 | |
期号 | 21页码:1994 |
关键词 | UAV multispectral remote sensing farmland objects classification RF SVM |
DOI | doi.org/10.3390/ s21061994 |
英文摘要 | 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’ 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’ 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 structur. |
出版地 | 瑞士 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.iswc.ac.cn/handle/361005/9842] |
专题 | 水保所2018届毕业生论文 |
通讯作者 | Wenting, Han |
作者单位 | 1.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences 2.College of Mechanical and Electronic Engineering, Northwest A&F University 3.College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences 4.Institute of Soil and Water Conservation, Chinese Academy of Sciences, Ministry of Water Resources |
推荐引用方式 GB/T 7714 | Qian, Ma,Wenting, Han,Shenjin, Huang,et al. Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images[J]. sensors,2021(21):1994. |
APA | Qian, Ma,Wenting, Han,Shenjin, Huang,Shide, Dong,Guang, Li,&Haipeng, Chen.(2021).Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images.sensors(21),1994. |
MLA | Qian, Ma,et al."Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images".sensors .21(2021):1994. |
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