A New Crop Classification Method Based on the Time-Varying Feature Curves of Time Series Dual-Polarization Sentinel-1 Data Sets

Multitemporal Sentinel-1 data sets are suitable for high-precision agricultural classification mapping due to its short revisit period and dual-polarization channels. At present, more and more attention has been paid to the multitemporal classification methods with feature curve matching, because the time-varying polarimetric characteristics show great potential to crop classification. However, current methods only use the variation of single intensity feature, and the indicators for evaluating similarity have not considered the effect of the variable growing seasons of different parcels. Based on this, a new method with feature curve matching is proposed, which uses the combination of multiple features and applies the discrete Fréchet distance and the Pearson distance to evaluate the similarity between two curves. The proposed method applies time-series Sentinel-1 images for crop classification in two study areas of Gansu province, China. The results show that the overall accuracies in two study areas of the proposed method are 94.98% and 90.20%, respectively. This method achieves higher classification accuracies, compared with the SVM classification method and some other methods with feature curve matching.