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27 November 2024 Peanut crop identification based on UAV remote sensing image and random forest
Ying Li, Tongxiao Li, Wensong Fang, Chunhui Zou
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Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134020D (2024) https://doi.org/10.1117/12.3048867
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China
Abstract
Peanut is considered a crucial economic crop, and accurately identifying its planting areas is regarded as vital for ensuring food and oil security. This study utilizes ultra-high spatial resolution unmanned aerial vehicle (UAV) imagery and the random forest (RF) method to classify multispectral imagery. Three types of features are combined to identify peanut crop and verify accuracy. Results indicate that using only multispectral features yields unsatisfactory accuracy. Introducing texture features significantly improves classification accuracy, particularly for spectral features alone. However, accurately identifying crop pixels at field edges remains challenging due to noise influence. When UAV image spectral and texture features are employed for classification, the principal component analysis (PCA) method, combined with post-classification patch removal, proves to be the best strategy, achieving an overall classification accuracy of 98.17% and a Kappa coefficient of 0.97. The comprehensive peanut recognition accuracy reaches 90.39%.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ying Li, Tongxiao Li, Wensong Fang, and Chunhui Zou "Peanut crop identification based on UAV remote sensing image and random forest", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134020D (27 November 2024); https://doi.org/10.1117/12.3048867
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KEYWORDS
Image classification
Unmanned aerial vehicles
Principal component analysis
Remote sensing
Land cover
Random forests
Spatial resolution
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