Winter Wheat Extraction Based on Short Time-series SAR Data with Dual Polarizations and Coherence Characteristics
To solve the problem that it is difficult to effectively monitor winter wheat in cloudy,rainy and foggy areas using the optical remote sensing data,an automatic method of extracting the planting area of winter wheat based on short time-series SAR data with dual polarizations is proposed.Based on the mean backscattering coefficient of HV/VV dual polarizations,InSAR coherence feature is introduced to establish a suitable classification feature dataset for extracting the winter wheat,which can better solve the problem of misclassification of rural trails.Then,the spatial distribution thematic map of the winter wheat in the study area is obtained based on random forest classifier.Taking Liangshan county,Shandong province as an experimental area,the planting area and spatial distribution of winter wheat are obtained by using 20 Sentinel-1 A SAR data with dual polarizations from October 2021 to June 2022.The results show that:the method based on the short time-series SAR data could meet the requirements of winter wheat monitoring,with the overall accuracy reaching 92.799％and the Kappa coefficient reaching 0.912;compared with no coherence feature,the overall accuracy is improved by 6.279％,and the Kappa coefficient is increased by 0.096;the short time-series SAR data of tassel stage can be used to replace the time-series SAR data of the full growth period,and the data calculation amount is greatly reduced,while the accuracy difference is only 3.008％;the second option is flowering and milking stage,and compared with the full growth period,the overall accuracy difference is only 3.341％.
short time-series SARwinter wheatdual polarizationcoherencerandom forest
万方数据
维普
