Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

doi:10.3390/s21061994

	Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images
	Ma, Qian 1,4; Han, Wenting 2,4; Huang, Shenjin 2; Dong, Shide 3; Li, Guang 2; Chen, Haipeng 2
刊名	SENSORS
	2021-03-01
卷号	21 期号:6 页码:22
关键词	UAV multispectral remote sensing farmland objects classification RF SVM
DOI	10.3390/s21061994
通讯作者	Han, Wenting(hanwt2000@126.com)
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 structures.
资助项目	13th Five-Year Plan for Chinese National Key RD Project[2017YFC0403203] ; National Natural Science Foundation of China[51979233]
WOS关键词	UNMANNED AERIAL VEHICLES ; RANDOM FOREST ; VEGETATION INDEXES ; CLASSIFICATION ; LIGHT
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	MDPI
WOS记录号	WOS:000652721000001
资助机构	13th Five-Year Plan for Chinese National Key RD Project ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162540]
专题	中国科学院地理科学与资源研究所
通讯作者	Han, Wenting
作者单位	1.Univ Chinese Acad Sci, Coll Adv Agr Sci, Beijing 100049, Peoples R China 2.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Inst Soil & Water Conservat, Minist Water Resources, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Ma, Qian,Han, Wenting,Huang, Shenjin,et al. Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images[J]. SENSORS,2021,21(6):22.
APA	Ma, Qian,Han, Wenting,Huang, Shenjin,Dong, Shide,Li, Guang,&Chen, Haipeng.(2021).Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images.SENSORS,21(6),22.
MLA	Ma, Qian,et al."Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images".SENSORS 21.6(2021):22.