Deep Learning Based Joint Reconstruction and Extraction of Urban Structures from Tomographic SAR Data

In this paper, we propose a method that allows to simultaneously extract urban structures and estimate scatterer heights and reflectivities from Tomographic SAR (TomoSAR) data. This method relies on a deep convolutional neural network and does not require any manual labelling. Training data is produced by simulating many simple ground/building scenes according to the acquisition geometry of the real dataset to be processed. It does not require prior tomographic focussing and operates on single-look complex (SLC) stacks. Our experiments lead to good agreement between the estimated and true values on simulated data and show promising results on real F-SAR data (DLR).