Deep-Learning-Based View Interpolation Toward Improved TomoSAR Focusing

Abstract

Synthetic aperture radar tomography (TomoSAR) uses several coregistered images from different perspectives to reconstruct a power spectrum pattern (PSP) perpendicular to the line of sight (PLOS), enabling the estimation of a 3-D representation of the area. Classical estimators exhibit ambiguities and other undesired effects that are stronger for sparser and smaller stacks. To mitigate the limitations arising from a restricted number of acquisitions, we propose using a deep neural network (NN) to synthesize artificial tracks (i.e., images not contained in the original stack). The presented method utilizes a convolutional NN with an encoder-decoder architecture. We evaluate the proposed approach on real TomoSAR data from an airborne campaign over a forest region. The view estimation improves the tomographic results, offering robustness to scenarios affected by temporal decorrelation, which other classical methods, such as cubic convolution (CC), do not provide.