Improved InSAR Phase Noise Filter in Frequency Domain

Suo, Zhiyong; Li, Ming; Zhang, Qingjun; Fang, Chao

doi:10.1109/tgrs.2015.2476355

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…Then, low-coherent areas will be filtered with higher values of α, while resolution will be preserved in high-quality areas. A pixel-by-pixel filter was proposed in [7]. That is, instead of performing a block-based filter, every pixel in the phase image is individually filtered with adaptive windows.…”

Section: Introductionmentioning

confidence: 99%

Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Suppression in Differential SAR Interferograms

Mestre-Quereda

López-Sánchez

Selva

et al. 2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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Differential SAR Interferometry (DInSAR) is a powerful remote sensing technique employed to monitor surface displacements, such as ground subsidence or strong deformations caused by geological activity. The quality of the interferometric phase between two combined SAR images is essential for the estimation of the surface deformation. Multiple decorrelation factors may degrade the quality of the measurements and, then, the development of filtering methods for noise suppression is mandatory. In this work, we propose a new strategy to improve noise reduction while preserving the original phase structure. The new method consists in an iterative filter in which noise reduction is achieved progressively. The original phase is filtered with adaptive kernels based on Chebyshev interpolation functions. The filter is especially useful for DInSAR geophysical applications, such as earthquakes or volcanic eruptions monitoring. The performance of the proposed method has been tested with both simulated data and recently acquired Sentinel-1 SAR data which mapped the August 2016 Central Italy earthquake.

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Section: Introductionmentioning

confidence: 99%

Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Suppression in Differential SAR Interferograms

Mestre-Quereda

López-Sánchez

Selva

et al. 2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

show abstract

“…However, the accuracy and reliability of InSAR measurements are highly dependent on the quality of interferometric phase. The interferogram phase is often contaminated by noises caused by the SAR imaging process and data processing, as well as temporal and baseline decorrelation effects [4] that impact the unwrapping process and decrease DEM accuracy [5].…”

Section: Introductionmentioning

confidence: 99%

“…Spatial filtering, such as the multi-looking procedure, reduces noises at the expense of spatial resolution [5]. To maintain the resolution, Lee et al [6] proposed a self-adaptive filter based on local gradient slope estimation, which can obtain satisfactory results by taking the images' directional characteristics into consideration.…”

Section: Introductionmentioning

confidence: 99%

SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics

Zhu

et al. 2017

Applied Sciences

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This paper presents a new filtering approach for Synthetic Aperture Radar (SAR) interferometric phase noise reduction in the shearlet domain, depending on the coherent statistical characteristics. Shearlets provide a multidirectional and multiscale decomposition that have advantages over wavelet filtering methods when dealing with noisy phase fringes. Phase noise in SAR interferograms is directly related to the interferometric coherence and the look number of the interferogram. Therefore, an optimal interferogram filter should incorporate information from both of them. The proposed method combines the phase noise standard deviation with the shearlet transform. Experimental results show that the proposed method can reduce the interferogram noise while maintaining the spatial resolution, especially in areas with low coherence.

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“…In practice, the quality of the generated interferogram is limited by phase noise due to co-registration errors, thermal noise, temporal decorrelation, baseline decorrelation, electromagnetic interference, and so on [12,13]. To reduce the phase unwrapping difficulty and improve the precision of the unwrapped phase, phase filtering has become an essential step for InSAR data processing [14,15,16]. An ideal phase filter should be able to reduce phase residues significantly while preserving the fringe details well [17].…”

Section: Introductionmentioning

confidence: 99%

Improved Goldstein Interferogram Filter Based on Local Fringe Frequency Estimation

Feng

et al. 2016

Sensors

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The quality of an interferogram, which is limited by various phase noise, will greatly affect the further processes of InSAR, such as phase unwrapping. Interferometric SAR (InSAR) geophysical measurements’, such as height or displacement, phase filtering is therefore an essential step. In this work, an improved Goldstein interferogram filter is proposed to suppress the phase noise while preserving the fringe edges. First, the proposed adaptive filter step, performed before frequency estimation, is employed to improve the estimation accuracy. Subsequently, to preserve the fringe characteristics, the estimated fringe frequency in each fixed filtering patch is removed from the original noisy phase. Then, the residual phase is smoothed based on the modified Goldstein filter with its parameter alpha dependent on both the coherence map and the residual phase frequency. Finally, the filtered residual phase and the removed fringe frequency are combined to generate the filtered interferogram, with the loss of signal minimized while reducing the noise level. The effectiveness of the proposed method is verified by experimental results based on both simulated and real data.

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Improved InSAR Phase Noise Filter in Frequency Domain

Cited by 35 publications

References 27 publications

Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Suppression in Differential SAR Interferograms

Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Suppression in Differential SAR Interferograms

SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics

Improved Goldstein Interferogram Filter Based on Local Fringe Frequency Estimation

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