A Locally Adaptive Filter of Interferometric Phase Images

Wu, Nan; Feng, Da‐Zheng; Li, Junxia

doi:10.1109/lgrs.2005.856703

Cited by 52 publications

(31 citation statements)

References 15 publications

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“…As the noise will influence the precision of similarity adjudgment, it is reasonable to use a higher threshold d hard to select enough similar patches, i.e., d hard = π 2 4 . In order to restrict the number of similar patches, the K-nearest neighbor strategy is also implemented.…”

Section: Processing Stepsmentioning

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: Processing Stepsmentioning

confidence: 99%

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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“…In practical InSAR processing procedures, the effects of the terrain changes must be mitigated or eliminated in order to improve the accuracy of InSAR phase estimation [11,12]. If a prior coarse DEM of the illuminated scene is available, we can compute the one-to-one correspondence between SAR images and the coarse DEM [13], removing the effects of topography and achieving more accurate InSAR phase estimation.…”

Section: Signal Modelmentioning

confidence: 99%

“…Consequently, we define the cost function as (t r ,t a ) = maximise t a, t r l K (t a, t r ) l K+1 (t a, t r ) (11) which is a two-dimensional optimisation problem. To reduce the computational load, in practical applications the azimuth local frequency t a and range local frequency t r are estimated separately.…”

Section: Signal Modelmentioning

confidence: 99%

Improving the accuracy of local frequency estimation for interferometric synthetic aperture radar interferogram noise filtering considering large coregistration errors

Guo

Zhang

Liu

et al. 2014

IET Radar, Sonar & Navigation

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This study deals with the problem of estimating the local frequencies for interferometric synthetic aperture radar (InSAR) phase image (i.e. interferogram) noise filtering considering large coregistration errors. The estimation of local frequencies is frequently applied to achieving high performance of interferometric phase noise filtering, which is a key step in InSAR processing procedures. Unfortunately, the generated interferograms suffer seriously from coregistration errors especially for complicated topographies, thus imposing strong limits on the accuracy of local frequencies estimation by the existing methods. Taking large coregistration errors into account, the proposed method makes full use of the neighbouring pixels to construct joint pixel vector and take the separation extent of the signal and noise subspaces as the criterion to accurately estimate the local frequencies so that the effects of the envelope misalignment can be mitigated. Theoretical analysis and the simulated data as well as the real ERS-1/2 data show that the presented method has the ability to provide accurate estimation of local frequencies even if the coregistration error reaches one pixel.

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“…Manuscript One of the simplest phase filtering methods is the mean filter, which has been broadly used in image processing. Adaptive schemes in filtering of the InSAR image have been proposed in [2]- [4] and most recently in [5], which employ a local statistics filter (LSF). The LSFs, which are also called the Lee filters, include both the additive (for the phase image) and the multiplicative (for the magnitude image or speckle) noise.…”

Section: Introductionmentioning

confidence: 99%

Signal Representation and the Nonlinear Property of Phase Images

Suksmono

2007

IEEE Geosci. Remote Sensing Lett.

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This letter proposes a representation of pixel value in a phase image as a complex number, which implies that the phase image is a nonlinear field. Consequently, conventional linear filtering will not properly work. Local statistics filters (LSFs) are chosen as a special case of the signal-processing algorithm to show the significance of signal representation to filtering performance. First, relationships among various LSFs, namely, ordinary LSF, speckle LSF, and phasor LSF, are discussed. We found that they are related to each other, whose general form is a complex-valued LSF (CV-LSF). It is also realized that directional windowing for the CV-LSF is actually not required, which indicates that the complex-valued formulation (representation and algorithm) is the most suitable one for phase image filtering. We demonstrate that the linear filtering to a simulated complex-valued image and an actual interferometric synthetic aperture radar image will start to fail when the window size is subsequently enlarged due to the nonlinear nature of the phase image.

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A Locally Adaptive Filter of Interferometric Phase Images

Cited by 52 publications

References 15 publications

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

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

Improving the accuracy of local frequency estimation for interferometric synthetic aperture radar interferogram noise filtering considering large coregistration errors

Signal Representation and the Nonlinear Property of Phase Images

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