A Brief Evaluation of InSAR Phase Denoising and Coherence Estimation with Phi-Net
Roland Akiki, Jérémy Anger, Carlo de Franchis, Gabriele Facciolo, Raphaël Grandin, Jean-Michel Morel
published
2024-07-26
reference
Roland Akiki, Jérémy Anger, Carlo de Franchis, Gabriele Facciolo, Raphaël Grandin, and Jean-Michel Morel, A Brief Evaluation of InSAR Phase Denoising and Coherence Estimation with Phi-Net, Image Processing On Line, 14 (2024), pp. 205–216. https://doi.org/10.5201/ipol.2024.549

Communicated by Pablo Musé
Demo edited by Roland Akiki

Abstract

In this article, we examine the joint InSAR phase denoising and coherence estimation performance of the network known as Phi-Net [Sica et al., IEEE Transactions on Geoscience and Remote Sensing, 2021]. We briefly examine the method, network architecture, training data and strategy. Then, in the experimental section, we compare the network's performance against the simple boxcar uniform filter. We verify the observations made by the authors, in particular concerning the superior denoising performance and preservation of fine details in the coherence estimation. Our experiments also indicate that an end-to-end deep learning method might bring a small improvement to the patch-based approach adopted in Phi-Net.

This is an MLBriefs article, the source code has not been reviewed!
The original source code is available here (last checked 2024/07/10).

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