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Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images
Qian Ma1,2, Wenting Han1,3, Shenjin Huang3
1Institute of Soil and Water Conservation, Chinese Academy of Sciences, Ministry of Water Resources, Yangling 712100, China.
Sensors (Basel, Switzerland)
|April 3, 2021
Summary
Unmanned aerial vehicle (UAV) multispectral remote sensing effectively classifies farmland, even with complex planting structures. The object-oriented support vector machine (OB-SVM) model showed superior accuracy and resilience to complexity changes compared to the object-oriented random forest (OB-RF) model.
Area of Science:
- Agricultural remote sensing
- Geospatial data analysis
- Machine learning in agriculture
Background:
- Farmland classification is crucial for agricultural management and monitoring.
- Increasingly complex planting structures pose challenges for traditional remote sensing classification.
- Unmanned aerial vehicle (UAV) multispectral imaging offers high-resolution data for detailed land cover analysis.
Purpose of the Study:
- To evaluate the classification performance of multispectral models for farmland with varying planting structure complexity.
- To compare the effectiveness of object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) models.
- To determine the suitability of UAV multispectral remote sensing for complex agricultural landscapes.
Main Methods:
- Acquisition of multispectral images using UAVs across low, medium, and high complexity planting structure study areas.
- Feature subset selection using recursive feature elimination (RFE).
- Development and evaluation of OB-RF and OB-SVM classification models using confusion matrices.
Main Results:
- Both OB-RF and OB-SVM models achieved high accuracies in low-complexity areas (97.09% and 99.13%, respectively).
- Classification accuracy decreased with increasing planting structure complexity for both models.
- The OB-SVM model demonstrated greater stability, with accuracy decreasing by only 1.92% compared to OB-RF's 8.1% decrease in high-complexity scenarios.
- OB-SVM achieved 97.21% overall accuracy in high-complexity areas with at least 85.65% single-crop extraction accuracy.
Conclusions:
- UAV multispectral remote sensing is a viable tool for farmland classification, even in highly complex planting structures.
- The OB-SVM model is more robust and accurate than OB-RF for classifying complex agricultural landscapes.
- The findings support the application of advanced remote sensing and machine learning techniques for precision agriculture.
