Skip to main navigation Skip to search Skip to main content

Phase Unwrapping using a Joint CNN and SQD-LSTM Network

  • Roland Akiki
  • , Carlo de Franchis
  • , Gabriele Facciolo
  • , Jean-Michel Morel*
  • , Raphaël Grandin
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

37 Downloads (CityUHK Scholars)

Abstract

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. © 2022 IPOL & the authors.
Original languageEnglish
Pages (from-to)378-388
JournalImage Processing On Line
Volume12
Online published7 Oct 2022
DOIs
Publication statusPublished - 2022
Externally publishedYes

Research Keywords

  • CNN
  • deep learning
  • demo
  • InSAR
  • LSTM
  • phase unwrapping

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-SA 3.0. https://creativecommons.org/licenses/by-nc-sa/3.0/

Fingerprint

Dive into the research topics of 'Phase Unwrapping using a Joint CNN and SQD-LSTM Network'. Together they form a unique fingerprint.

Cite this