Convolutional deep learning network for InSAR phase denoising and unwrapping

Asma Fejjari; Gianluca Valentino; Johann A. Briffa; Reuben A. Farrugia

doi:10.1117/12.2678684

19 October 2023 Convolutional deep learning network for InSAR phase denoising and unwrapping

Asma Fejjari, Gianluca Valentino, Johann A. Briffa, Reuben A. Farrugia

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330S (2023) https://doi.org/10.1117/12.2678684
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Interferometric phase unwrapping is one of the most challenging research topics for the remote sensing community. Recovering and correctly estimating the true interferometric phase signal from the received wrapped one provides critical information about changes in the Earth’s surface over time. Interferometric synthetic aperture radar (InSAR) has been widely used to extract such displacement estimates. However, InSAR images are affected often by a particular type of noise known as Gaussian. The presence of Gaussian noise in InSAR data can make the phase unwrapping process more difficult. In this paper, we introduce a convolutional deep learning-based network to perform simultaneous interferometric phase denoising and unwrapping. Quantitative and qualitative evaluations, made on synthetic and real world InSAR data, show that the proposed approach is able to produce accurate results even in the presence of strong noise.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Asma Fejjari, Gianluca Valentino, Johann A. Briffa, and Reuben A. Farrugia "Convolutional deep learning network for InSAR phase denoising and unwrapping", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330S (19 October 2023); https://doi.org/10.1117/12.2678684

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