Phase Unwrapping using a Joint CNN and SQD-LSTM Network

Phase unwrapping techniques are used in various applications, including Synthetic Aperture Radar (SAR) interferometry (InSAR). Deep learning methods have been recently proposed to tackle this problem. This work aims at explaining and evaluating the method proposed by Perera et al. in [A joint convolutional and spatial quad-directional LSTM network for phase unwrapping, ICASSP 2021]. Furthermore, we provide an online demo to simulate phase images and run them through the network. The network performance can be tested visually and through metrics such as the error standard deviation. The simulation can provide some out-of-distribution data, especially with the added atmospheric signal specific to the InSAR phase. **This is an MLBriefs article, the source code has not been reviewed!** **The original source code is [[available here|https://github.com/Laknath1996/DeepPhaseUnwrap/tree/9316c1b1272c0457000fbbaaff850303d923df88]] (last checked 2022/10/04).**