Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 12, Issue 9, Pages 3295-3306Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2019.2922469
Keywords
Crop type identification; machine learning; random forest (RF); Sentinel-2A; support vector machine (SVM)
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Funding
- National Natural Science Foundation of China [41771450, 41871330, 41630749, 41571078, 41571489, 41601438]
- Fundamental Research Funds for the Central Universities [2412019BJ001]
- Foundation of the Education Department of Jilin Province in the 13th Five-Year Project [JJKH20190282KJ]
- National Key Research and Development Project [2016YFA0602301]
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In this paper, the random forests method and the support vector machine in machine learning are explored and compared to the traditional statistical-based maximum likelihood method with 126 features from Sentinel-2A images. The spectral reflectance of 12 bands, 96 texture parameters, 7 vegetation indices, and 11 phenological parameters are successfully extracted from Sentinel-2A images in 2017. The classification result shows that the optimal combination of 13 features yields overall accuracies of traditional classification and machine learning classification of 88.96% and 98% respectively. Short-wave infrared information shows a significant effect on distinguishing rice, corn, and soybean. The water vapor band plays a significant role in distinguishing between corn and rice. In the multiclassification problem, the machine learning methods have robustness with the identification accuracy of greater than 95% for each crop type, whereas the traditional classification result shows imbalanced accuracies for different crops.
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