Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring

Bhateja, Vikrant; Tripathi, Abhishek; Gupta, Anurag; Lay-Ekuakille, A.

doi:10.1016/j.measurement.2015.07.024

Cited by 47 publications

(3 citation statements)

References 37 publications

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“…Wu, X. Zhang and G. Ye are with the Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong development of SAR satellites can be traced back to the early 1950s when radar technology was first applied to satellite systems. It can provide high-resolution images for various purposes, such as environmental monitoring [8], resource management [9], disaster management [10], land use [11], and emergency response [12]. However, when dealing with SAR data, there still existed some room for improvements, such as the massive data volume, long processing time and sophisticated image alignment [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Efficient Management and Processing of Massive InSAR Images Using an HPC-Based Cloud Platform

Wu,

Ma,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Significant progress has occurred in Interferometric Synthetic Aperture Radar (InSAR), emerging as a crucial technique for monitoring surface deformation. This evolution is attributed to expanded Synthetic Aperture Radar (SAR) data availability and improved data quality. However, effectively managing and processing SAR big data presents substantial challenges for algorithms and pipelines, especially in large-scale contexts. In this paper, we introduce a parallel time-series InSAR processing platform that leverages High-Performance Computing (HPC) clusters for efficiently managing and processing large-scale SAR data, and incorporates Graphics Processing Unit (GPU) acceleration to significantly enhance the speed and efficiency of specific InSAR processing algorithms. Our approach encompasses high-quality data compression, integration of classic InSAR models, and the introduction of a robust Distributed Scatterer InSAR method for time-series processing. The platform efficiently handles massive data, featuring a parallel optimization tool for acceleration. Additionally, it provides web-based 2D result visualization and 3D outcome representation for comprehensive user understanding. To illustrate our platform's capabilities, we applied it to 40 Sentinel-1 SAR data scenes from Tibet (2017-2019). Our data compression technique notably reduces data size, reducing mask data by 87.5% and coherence data to 25% of its original size. Leveraging HPC and GPU, we achieved a 50% reduction in registration computation time. This study offers valuable insights and a comprehensive platform for InSAR practitioners, facilitating calculations and enhancing comprehension of surface deformation processes. Our system's improved processing efficiency, coupled with a variety of InSAR methods, makes it an alternative choice for InSAR data handling and analysis.

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Section: Introductionmentioning

confidence: 99%

Efficient Management and Processing of Massive InSAR Images Using an HPC-Based Cloud Platform

Wu,

Ma,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Although optical images and SAR images have different scattering characters, many denoising methods for optical images have been extended to speckle suppression. These methods include wavelet method [5, 6], variational method [7–10], partial differential equation (PDE) method [11–15], statistical method [16–19]. Different approaches have different starting points.…”

Section: Introductionmentioning

confidence: 99%

Sparse PDE for SAR image speckle suppression

Wang

Tan

et al. 2017

IET Image Processing

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“…However, the prior information of interferometric phase is required. An anisotropic diffusion filter is embedded into the wavelet domain [17] to achieve a robust speckle suppression at wide range of speckle variances. A sparse reconstruction method using simultaneous sparse coding (SSC) on the ordered patches [18] is proposed for multichannel POLSAR image filtering.…”

Section: Introductionmentioning

confidence: 99%

A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

Fang

Wang

et al. 2016

Remote Sensing

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An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l 1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes.

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Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring

Cited by 47 publications

References 37 publications

Efficient Management and Processing of Massive InSAR Images Using an HPC-Based Cloud Platform

Efficient Management and Processing of Massive InSAR Images Using an HPC-Based Cloud Platform

Sparse PDE for SAR image speckle suppression

A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

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