A Parameter-Free Enhanced SS&E Algorithm Based on Deep Learning for Suppressing Azimuth Ambiguities

Abstract

Aliasing artifacts introduced by azimuth ambiguity seriously impact the interpretation of synthetic aperture radar images. To achieve parameter-free and fast azimuth ambiguity suppression, a novel deep learning model is designed to estimate the ambiguous signal intensity to total signal intensity ratio in the range-Doppler domain. This model does not depend on processing parameters and can be applied in any acquisition mode. The mean shift algorithm is applied to select less ambiguous subspectra according to the estimation result. The selected subspectra are restored to a full spectrum with an energy concentrated extrapolation method to preserve the resolution. The enhanced spectral selection and extrapolation algorithm overcomes the dependence on processing parameters, and experiments based on TerraSAR-X and Radarsat-2 images indicate that the proposed algorithm suppresses the azimuth ambiguity significantly.