  Synthetic aperture radar tomographic imaging reconstructs the
three-dimensional reflectivity of a scene from a set of coherent acquisitions
performed in an interferometric configuration. In forest areas, a large number
of elements backscatter the radar signal within each resolution cell. To
reconstruct the vertical reflectivity profile, state-of-the-art techniques
perform a regularized inversion implemented in the form of iterative
minimization algorithms. We show that light-weight neural networks can be
trained to perform the tomographic inversion with a single feed-forward pass,
leading to fast reconstructions that could better scale to the amount of data
provided by the future BIOMASS mission. We train our encoder-decoder network
using simulated data and validate our technique on real L-band and P-band data.
