Classification Performance Comparison of Time-Variant Scattering Features of Multi-Temporal Polarimetric SAR Data

The multi-temporal polarimetric SAR data provides the difference of scattering characteristics in time dimension for terrain classification, hence it could reflect the time-variant characteristics of the same scene. Based on this advantage, crop classification is one of the important applications of multi-temporal polarimetric SAR data. However, the features of time and polarization dimension used for classification basically are from the data at each certain time, which lack the interpretation of the variant characteristics between multi-temporal data. To solve the problem, based on the specific data representation models for multi-temporal polarimetric SAR data, this paper extracts new time variant scattering features, including the change type as well as the change direction (increase or decrease). Time series Radarsat-2 data is applied for scattering change interpretation. By using the proposed features to classify crops, it is proved that the method can effectively improve the classification accuracy, and the classification performance of difference data representation models is compared.