Coherence Matrix Power Model for Scattering Variation Representation in Multi-Temporal PolSAR Crop Classification

The multitemporal polarimetric SAR (PolSAR) data contains the scattering change information during the growth of crops. However, the current classification methods usually directly use the addition of features extracted at single-temporal or use the temporal and spatial variations of certain features, not really exploring the complete scattering variation information. The specific data representation models for multitemporal PolSAR data should combine time with polarimetry to characterize the scattering variations. However, the characterization and utilization of such kind of models are inadequate. In this article, we construct data representation model based on the power form of coherence matrix to comprehensively represent all kinds of scattering mechanism variation, which is full-rank positive semidefinite Hermitian matrix. We extract new time-variant scattering features and design vision transformer classifier accordingly for crop classification. Experiment results on RADARSAT-2 datasets show that the proposed power representation model outperforms other models.