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Synthetic aperture radar (SAR) interferometry (InSAR) is performed usingrepeat-pass geometry. InSAR technique is used to estimate the topographicreconstruction of the earth surface. The main problem of the range-Dopplerfocusing technique is the nature of the two-dimensional SAR result, affected bythe layover indetermination. In order to resolve this problem, a minimum of twosensor acquisitions, separated by a baseline and extended in thecross-slant-range, are needed. However, given its multi-temporal nature, thesetechniques are vulnerable to atmosphere and Earth environment parametersvariation in addition to physical platform instabilities. Furthermore, eithertwo radars are needed or an interferometric cycle is required (that spans fromdays to weeks), which makes real time DEM estimation impossible. In this work,the authors propose a novel experimental alternative to the InSAR method thatuses single-pass acquisitions, using a data driven approach implemented by DeepNeural Networks. We propose a fully Convolutional Neural Network (CNN)Encoder-Decoder architecture, training it on radar images in order to estimateDEMs from single pass image acquisitions. Our results on a set of Sentinelimages show that this method is able to learn to some extent the statisticalproperties of the DEM. The results of this exploratory analysis are encouragingand open the way to the solution of single-pass DEM estimation problem withdata driven approaches.

Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks - Application on Synthetic Aperture Radar Images