Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration

Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration. These methods generally follow the nonlocal filtering processing chain, aiming at circumventing the staircase effect and pres...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 32; číslo 2; s. 826 - 840
Hlavní autoři: Kang, Jian, Hong, Danfeng, Liu, Jialin, Baier, Gerald, Yokoya, Naoto, Demir, Begum
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Coding
Convolution
Convolutional codes
Convolutional dictionary learning
Dictionaries
Encoding
Image restoration
Interferometry
Machine learning
Noise reduction
nonlocal filtering
Phase variations
Restoration
SAR interferometry (InSAR)
Source code
sparse coding (SC)
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration. These methods generally follow the nonlocal filtering processing chain, aiming at circumventing the staircase effect and preserving the details of phase variations. In this article, we propose an alternative approach for InSAR phase restoration, that is, Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version. To the best of the authors' knowledge, this is the first time that we solve the InSAR phase restoration problem in a deconvolutional fashion. The proposed methods can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. Furthermore, they provide an insight into the elementary phase components for the interferometric phases. The experimental results on synthetic and realistic high- and medium-resolution data sets from TerraSAR-X StripMap and Sentinel-1 interferometric wide swath mode, respectively, show that our method outperforms those previous state-of-the-art methods based on nonlocal InSAR filters, particularly the state-of-the-art method: InSAR-BM3D. The source code of this article will be made publicly available for reproducible research inside the community.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.2979546