An Adaptive Contoured Window Filter for Interferometric Synthetic Aperture Radar

Yu, Qifeng; Yang, X. H.; Fu, Shaojing; Liu, Xiaolin; Sun, Xiangyi

doi:10.1109/lgrs.2006.883527

Cited by 91 publications

(49 citation statements)

References 12 publications

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“…The estimation of clean coefficients is expressed as a Bayesian estimation problem, such as the MAP estimator. In Equation (6), β i is treated as a peer hidden variable that can be utilized to promote more accurate shrinkage method. Under the Bayesian formula, the posterior estimator is formulated as…”

Section: −1mentioning

confidence: 99%

“…From the point view of the Bayesian estimation, we can see that Equation (11) is equivalent to Equation (6). The regularization term of α i − β i p should use the l 1 norm (p = 1).…”

Section: −1mentioning

confidence: 99%

“…Examining the approaches carefully, one reveals some deficiencies. For instance, the direct filtering methods [1,2] applied in the spatial domain may not preserve details of fringes although the window direction-dependent [3] and size-dependent [4][5][6] methods are able to remedy the preservation difficulty to some degree. With the assumption that the true signal and noise could be separated in the frequency domain after transformation, the denoising is performed by suppressing part of the transformed coefficients.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: −1mentioning

confidence: 99%

“…From the point view of the Bayesian estimation, we can see that Equation (11) is equivalent to Equation (6). The regularization term of α i − β i p should use the l 1 norm (p = 1).…”

Section: −1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Fang

Wang

et al. 2016

Remote Sensing

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show abstract

“…(3) After adding all principal vectors, which is 2D weighted, the nonlinear phaseφ p can be estimated according to Equation (12).…”

Section: Application Of Nonlinear Phase Model In Filteringmentioning

confidence: 99%

“…These can be divided into two categories: spatial domain filter and transform domain filter. The spatial domain filter has median filter, mean filter, adaptive filter with directionally dependent windows [4], adaptive median filter [5], the zero intermediate frequency vector filter [6], slope compensated filter [7][8][9][10][11], and adaptive contoured window filter [12]. Due to the circular periodic feature of the interferometric fringes, the interferometric phases will be in complex exponential form in these filters.…”

Section: Introductionmentioning

confidence: 99%

A Method of Filtering and Unwrapping Sar Interferometric Phase Based on Nonlinear Phase Model

Huang¹,

Wang²

2014

PIER

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Abstract-This paper presents a new efficient algorithm of filtering and unwrapping phase images for interferometric synthetic aperture radar (InSAR) based on nonlinear phase model. First, we analyzed the statistical and signal properties of interferometric phase, and proposed the concept of nonlinear phase model. The model of reflecting topographic contour is used to approximate the interferometric phase variation occuring over the local window. And the lower amplitude bound of the principal vector is decided and the value solution method is given, which can solve effectively and adaptively the nonlinear factor of the phase. Second, we studied the application of nonlinear phase model in interferometric phase filtering. When compared with other advanced filters, the nonlinear phase compensation filter, with a higher computation efficiency and a better phase estimation accuracy in low coherence areas, has a stronger ability to reduce interferometric phase noise in rugged terrain. Finally, we introduced the nonlinear phase model to phase unwrapping, which increased the reliability of integration path and the accuracy of the phase gradient, and improved effectively the performance of phase unwrapping. And the real data processing results demonstrated the validity of the proposed nonlinear phase model and of the corresponding solutions.

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3D Digital Elevation Model Generation

Wei

Bartels

2012

3D Imaging, Analysis and Applications

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An Adaptive Contoured Window Filter for Interferometric Synthetic Aperture Radar

Cited by 91 publications

References 12 publications

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

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

A Method of Filtering and Unwrapping Sar Interferometric Phase Based on Nonlinear Phase Model

3D Digital Elevation Model Generation

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