Multi-scale feature extraction approaches for classification of insar and phase gradient insar images

This paper investigates different multi-scale approaches in terms of feature extraction for classification of interfero-metric SAR (InSAR) and previously defined phase gradient InSAR (PGInSAR) images. For this purpose, the scale-space image representation approach is implemented together with the two partial derivative based structure matrices, namely the Hessian matrix and second moment matrix. Their performance is compared to two other multi-scale approaches, namely the Gabor and Fractional Fourier Transform (FrFT) based features, which are quite successful for classification of SAR, InSAR and PGInSAR images. The supervised classification experiments show that the use of PGInSAR images together with the partial derivative based scale-space image representation achieves the best results among all, with a mean accuracy of 90.31% and individual class accuracies more than 80%, even reaching 99% for urban scenes.