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The interpretation of multidimensional synthetic aperture radar (SAR) data often requires expert knowledge. In fact, it requires to simultaneously consider several time series of polarimetric features to understand the physical changes of a target and its temporal evolution. In an effort to characterize the changes over time, multitemporal polarimetric SAR (MTPolSAR) change detection was introduced in the literature. However, existing methods either only exploit intensity of changes or the resulting changed scattering mechanisms are not guaranteed to represent physical changes of the target. This article presents a variation in a previously published change detector based on the difference of covariance matrices that characterize the polarimetric information, allowing for an intuitive representation and characterization of physical changes of a target and its dynamics. We show the results of this method for monitoring growth stages of rice crops and present a novel application of the method for crop type mapping from MT-PolSAR data. We compare its performance with a neural network based classifier that uses time series of PolSAR features derived from a target covariance matrix decomposition as input. Experimental results show that the classification performance of the proposed method and the baseline method are comparable with differences between the two methods in the overall balanced accuracy and the F1-macro metrics of around 2&#x0025; and 3&#x0025;, respectively. The method presented here achieves similar classification performances of a traditional PolSAR data classifier while providing additional advantages in terms of interpretability and insights about the physical changes of a target over time.

Multitemporal Polarimetric SAR Change Detection for Crop Monitoring and Crop Type Classification