Complex-Valued Autoencoders with Coherence Preservation for SAR

Deep learning techniques have attracted many interests in various fields, including Synthetic Aperture Radar (SAR). A few researches have extended the deep learning framework into the complex-domain to exploit the phase and amplitude of the Complex-Valued (CV)-SAR. In this study, a complex-valued Convolutional AutoEncoder (CV-CAE) is developed for CV-SAR data reconstruction from the lower resolution azimuth subaperture images. Finally, the performance of the CV-CAE is evaluated, in terms of the reconstruction quality and the coherence preservation, and showed that the CV-CAE is capable of reconstructing the CV-SAR image and preserving the coherency, with a remarkably small training dataset.