?-Net: Deep Residual Learning for InSAR Parameters Estimation

Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and enhancement signals disrupted by noise different natures. In this article, we address problem interferometric parameters from synthetic aperture radar (SAR) data. particular, combine convolutional neural networks together with concept residual to define novel architecture, named ?-Net, for joint phase coherence. ?-Net is trained using data obtained an innovative strategy based on theoretical modeling physics behind SAR acquisition principle. This allows network generalize respect to: 1) levels; 2) nature imaged target ground; 3) geometry. We then analyze performance independent set synthesized data, as well real InSAR TanDEM-X Sentinel-1 missions. The proposed architecture provides better results state-of-the-art algorithms both test Finally, perform application-oriented study retrieval topographic information, shows that strong candidate generation high-quality digital elevation models at resolution close one original single-look complex