Remote Sensing Identification of Alfalfa Based on Sentinel Data and Machine Learning Algorithms
[Objective]In order to accurately get information on the spatial distribution of alfalfa,so as to provide security supply status of fodder grasses as well as technical support for related authorities.[Methods]Datasets of spectral features,vegetation indices and radar polarization features were constructed by Sentinel-1 and Sentinel-2 data of Sentinel satellites that were acquired and processed from the Google Earth Engine platform.Then four classification algorithms consisting of decision tree,random forest,support vector machine and deep learning,were utilized to evaluate the remote sensing classification accuracy of different feature combinations,so as to screen out the optimal feature combinations and remote sensing classification model of alfalfa.[Results]The results showed that:The deep neural network model combining spectral features,vegetation indices and radar polarization features performed optimally in alfalfa remote sensing classification,with an overall accuracy of 94.85%and a Kappa coefficient of 94.2%.The study demonstrated the effectiveness of machine learning methods in improving the accuracy of remote sensing classification of alfalfa.The combination of spectral features and radar features had the highest accuracy for all four classification models,indicating the importance of multi-source remote sensing information for improving model performance.[Conclusion]The deep neural network-based remote sensing identification model for alfalfa was best under the condition of combining the spectral features,vegetation indices and radar polarization features.
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