InSAR Phase Denoising: A Review of Current Technologies and Future Directions

• URL: http://arxiv.org/abs/2001.00769v2
• Date: Sat, 19 Dec 2020 08:58:10 GMT
• Title: InSAR Phase Denoising: A Review of Current Technologies and Future Directions
• Authors: Gang Xu, Yandong Gao, Jinwei Li and Mengdao Xing
• Abstract summary: Interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. Phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories.
• Score: 9.475024122649288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing.

Related papers

• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach. First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms. Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates. Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo Labeling [29.075757272606868]
  We introduce the first voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation.
  arXiv  Detail & Related papers  (2024-06-25T00:16:57Z)
• Towards Generalizable Deepfake Detection by Primary Region Regularization [52.41801719896089]
  This paper enhances the generalization capability from a novel regularization perspective. Our method consists of two stages, namely the static localization for primary region maps, and the dynamic exploitation of primary region masks. We conduct extensive experiments over three widely used deepfake datasets - DFDC, DF-1.0, and Celeb-DF with five backbones.
  arXiv  Detail & Related papers  (2023-07-24T05:43:34Z)
• WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer [3.832032989515628]
  We propose WNet, a data-driven dual-domain denoising model which contains a trainable reconstruction layer for sparse-view artifact denoising. We train and test our network on two clinically relevant datasets and we compare the obtained results with three different types of sparse-view CT denoising and reconstruction algorithms.
  arXiv  Detail & Related papers  (2022-07-01T13:17:01Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• A SAR speckle filter based on Residual Convolutional Neural Networks [68.8204255655161]
  This work aims to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs) The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)
  arXiv  Detail & Related papers  (2021-04-19T14:43:07Z)
• Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview [89.09270073549182]
  Terahertz (THz) signal generation and radiation methods are shaping the future of wireless systems. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. We present an overview of these techniques, with an emphasis on signal pre-processing. We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band.
  arXiv  Detail & Related papers  (2021-04-09T01:38:34Z)
• Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration [22.705150812947537]
  We propose an alternative approach for InSAR phase restoration, i.e. Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version. The proposed methods can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. 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.
  arXiv  Detail & Related papers  (2020-03-06T21:01:44Z)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)
• CNN-based InSAR Coherence Classification [3.562355298993529]
  We introduce Convolutional Neural Networks (CNNs) to improve coherence-based demarcation and reduce misclassifications in completely incoherent regions. We show their effectiveness in improving coherence-based demarcation and reducing misclassifications in completely incoherent regions through intelligent preprocessing of training data.
  arXiv  Detail & Related papers  (2020-01-20T03:25:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.