Winter Wheat Planting Information Extraction Based on Principal Component and Cloud Model

Based on preprocessing of Sentinel-2 satellite remote sensing images, main information of wheat was extracted by principal component changes, and then a spectral remote sensing image classification method based on cloud model was applied．When classifying, firstly, according to training sample set, cloud model of typical wheat was generated by reverse cloud generator, and then cloud generator was used to calculate average membership degree of each pixel in each band to wheat ground features．On basis of analysis, the first principal component containing complex information was discarded, and the second and third principal component information was used to extract spatial information of winter wheat planting．Results showed that mapping accuracy and user accuracy of extracting wheat planting information were 92.78% and 99.90%, respectively．Membership value of wheat planting fields had great differences due to difference of wheat growth and density．Cloud model of poor growth and density, which resulted in low wheat pixels, had phenomenon of missing points．In general, algorithm based on cloud model was highly accurate, and there were few misclassifications and omissions in identification of wheat field．Algorithm would play an important supporting role in agricultural sector for winter wheat growth monitoring and yield estimation．