Abstract
In this article, we examine the joint InSAR phase denoising and coherence estimation performance of the network known as Φ-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 Φ-Net. © 2024 IPOL & the authors.
| Original language | English |
|---|---|
| Pages (from-to) | 205-216 |
| Journal | Image Processing On Line |
| Volume | 14 |
| Online published | 26 Jul 2024 |
| DOIs | |
| Publication status | Published - 2024 |
Bibliographical note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).Research Keywords
- CNN
- coherence estimation
- demo
- InSAR
- phase denoising
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/
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