A Deep-Learning Approach for SAR Tomographic Imaging of Forested Areas

Synthetic aperture radar tomographic imaging reconstructs the 3-D reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a high 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 lightweight neural networks can be trained to perform this 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.