Deep Learning-Based Likelihood Phase Unwrapping for Multi-Baseline InSAR Interferograms

Abstract

Multibaseline (MB) interferometric synthetic aperture radar (InSAR) is an advanced variant of conventional InSAR that aims to enhance the accuracy and reliability of phase unwrapping (PU). Among the PU methods employed in MB-InSAR, the maximum likelihood (ML) method offers an optimal solution for phase estimation. However, its limited noise robustness has hindered its practical applicability. To address this limitation, we propose a novel approach, named InSAR phase probability density function (PDF)-to-height/deformation (PDF2HD), which leverages a newly introduced deep convolutional neural network (DCNN) with exceptional anti-noise capabilities. The PDF2HD method employs U-Net and residual network to estimate the InSAR PDF, enabling it to mitigate the influence of phase noise. We present experimental results using two simulated MB InSAR datasets to demonstrate the effectiveness of our proposed method for both digital elevation model (DEM) reconstruction and deformation monitoring.