Scene recognition based on phase gradient InSAR images

Automated recognition of SAR images requires feature extraction from complex-valued data. In this work, complex-valued interferometric SAR (InSAR) images, which are mainly used to construct elevation models, are proposed for feature extraction. Feature extraction based on the log-cumulants of fractional Fourier transform (FrFT) coefficients has already been proposed in the literature and found to be quite successful for the classification of single-look complex (SLC) SAR images. Here, this method is applied to the complex-valued InSAR and newly introduced complex-valued phase gradient InSAR (PGInSAR) images. In order to evaluate the classification performance for SLC, InSAR and PGInSAR images, a database of bistatic TanDEM-X interferometric pairs is constructed, and a supervised KNN classification is performed. The overall classification accuracies for a 4% training set size show that the use of InSAR and PGInSAR images outperforms SLC images by 15% and 24%, respectively. In terms of individual class accuracies, the biggest improvement is observed in agricultural fields and mixed vegetation areas.