  Synthetic aperture radar (SAR) interferometry (InSAR) is performed using
repeat-pass geometry. InSAR technique is used to estimate the topographic
reconstruction of the earth surface. The main problem of the range-Doppler
focusing technique is the nature of the two-dimensional SAR result, affected by
the layover indetermination. In order to resolve this problem, a minimum of two
sensor acquisitions, separated by a baseline and extended in the
cross-slant-range, are needed. However, given its multi-temporal nature, these
techniques are vulnerable to atmosphere and Earth environment parameters
variation in addition to physical platform instabilities. Furthermore, either
two radars are needed or an interferometric cycle is required (that spans from
days to weeks), which makes real time DEM estimation impossible. In this work,
the authors propose a novel experimental alternative to the InSAR method that
uses single-pass acquisitions, using a data driven approach implemented by Deep
Neural Networks. We propose a fully Convolutional Neural Network (CNN)
Encoder-Decoder architecture, training it on radar images in order to estimate
DEMs from single pass image acquisitions. Our results on a set of Sentinel
images show that this method is able to learn to some extent the statistical
properties of the DEM. The results of this exploratory analysis are encouraging
and open the way to the solution of single-pass DEM estimation problem with
data driven approaches.
