Winter wheat planting area extraction using SAR change detection

Abstract

ABSTRACT With its all-day and all-weather imaging capability, Synthetic Aperture Radar (SAR) has shown great potential in crop identification. This letter puts forward a novel method of extracting winter wheat planting area by means of SAR change detection. Based on the phenological differences between winter wheat and other crops, the Difference Image (DI) is generated firstly by the different temporal SAR images. Then, Object Markov Random Field (OMRF) model is used to DI pre-classification. Finally, Convolutional Neural Network (CNN) is utilized to obtain the better area extraction result of winter wheat. The area extraction accuracy is proved by the field survey data, with an accuracy of 90.53%. This research can provide a new idea and method for agricultural remote sensing monitoring.