Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration
This addresses noise suppression and detail preservation in interferometric phase data for remote sensing applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles interferometric phase restoration in InSAR by proposing Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version, achieving superior performance over state-of-the-art methods like InSAR-BM3D on synthetic and real datasets from TerraSAR-X and Sentinel-1.
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 paper, we propose an alternative approach for InSAR phase restoration, i.e. Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version. To our best 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 of the elementary phase components for the interferometric phases. The experimental results on synthetic and realistic high- and medium-resolution datasets 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 paper will be made publicly available for reproducible research inside the community.