Title | ||
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Distinguishing Planting Structures Of Different Complexity From Uav Multispectral Images |
Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.3390/s21061994 | SENSORS |
Keywords | DocType | Volume |
UAV, multispectral remote sensing, farmland objects, classification, RF, SVM | Journal | 21 |
Issue | ISSN | Citations |
6 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Qian Ma | 1 | 0 | 0.34 |
Wenting Han | 2 | 2 | 2.77 |
Shenjin Huang | 3 | 0 | 0.68 |
Shide Dong | 4 | 0 | 0.34 |
Guang Li | 5 | 1 | 1.03 |
Haipeng Chen | 6 | 1 | 0.72 |