Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration

Abstract

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.

References in corpus (13)

• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
• Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
• Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
• Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification
• CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
• Convolutional Dictionary Learning: A Comparative Review and New Algorithms
• ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
• Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
• Iterative Bilateral Filtering of Polarimetric SAR Data
• Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition
• First and Second Order Methods for Online Convolutional Dictionary Learning
• Online Convolutional Dictionary Learning
• Non-Common Band SAR Interferometry via Compressive Sensing

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• Technical Report: Towards Spatial Feature Regularization in Deep-Learning-Based Array-SAR Reconstruction
• Hyperspectral Image Classification with Attention Aided CNNs
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• Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
• Spatial-Spectral Manifold Embedding of Hyperspectral Data