Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: An Alias-Free Method

Abstract

A new approach based on deep-learning methods is presented for reconstructing L-band brightness temperature images from the inversion of interferometric data, namely complex visibilities here simulated from observations of the Soil Moisture and Ocean Salinity (SMOS) interferometric radiometer. A specific Deep Neural Network (DNN) architecture composed of a fully connected followed by a contracting and expansive path is proposed to learn the relationship between the simulated visibilities and the brightness temperature maps. The performances of the DNN are compared to those of algebraic inversions based on Fourier theory, which are all affected by strong aliases in the synthesized field of view (FOV), as a consequence of the spacing between the elementary antennas of SMOS which does not satisfy the Nyquist criteria. In the alias-free FOV (AFFOV) of the algebraic reconstructions, these latter are outperformed by the DNN reconstructions: average mean absolute error (MAE) about 0.7 K for the DNN instead of 3.7 K. Outside the AFFOV of the algebraic reconstructions, the DNN reconstructions do not show significant signs of field aliasing although the MAE increases: average MAE about 1.5 K in the whole FOV. An analysis of the role of different neurons in the hidden layers is presented