Slope‐compensated interferogram filter with ESPRIT for adaptive frequency estimation

Li, Shijin; Zhang, Shubi; Gao, Yandong; Zhang, Qiuzhao

doi:10.1049/iet-ipr.2018.5506

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Herein, the time complexity is high because of the need to estimate the local terrain phase and parameters of the adaptive AGF. Li et al 49 proposed an adaptive slope compensated interferogram filtering method, the window size is selected adaptively according to the correlation coefficient and variance, and good results are achieved. Yang et al 50 proposed an interferogram filtering method with multilayer feature fusion neural networks.…”

Section: Interferogram Filtering Methodsmentioning

confidence: 99%

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

Huang,

Ding,

Wang

et al. 2023

Advances in Mechanical Engineering

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Interferometric synthetic aperture sonar (InSAS) is a novel three dimensional underwater mapping system, which is widely used in various fields. Interferometric phase filtering is a crucial process in InSAS signal processing. The study of interferogram filtering method has great significance to improve the signal-to-noise ratio (SNR) of interferogram, reduce the difficulty of phase unwrapping, and improve the accuracy of digital elevation maps. Firstly, this article introduces the sources of interferometric phase noise. Secondly, it illustrates the difference between real domain filter and complex domain filter by simulation experiment. Thirdly, the various methods of InSAS interferogram filtering are systematically studied, which can be classified into spatial domain filtering and transform domain filtering. The transform domain filtering can be further divided into frequency domain and wavelet transform filtering. Moreover, the advantages and disadvantages of various methods are analyzed theoretically. Finally, numerical simulation and real-data processing experiments are performed, and the advantages and shortcomings of several methods are compared using residue numbers, root mean square error (RMSE), and CPU time as evaluation criteria.

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Section: Interferogram Filtering Methodsmentioning

confidence: 99%

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

Huang,

Ding,

Wang

et al. 2023

Advances in Mechanical Engineering

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“…Over the past two decades, a large number of adaptive filtering methods [33], [34] has been proposed based on the spatial and frequency domains to balance phase noise suppression and phase fringe information preservation. On the basis of previous studies [35], we propose an efficient and effective adaptive filtering method by combining principal phase component estimation and fast nonlocal means filtering.…”

Section: A Spatial Adaptive Filteringmentioning

confidence: 99%

“…The latter is derived by the following rule [36]: The size of the local patch in frequency analysis ( 12) greatly affects the strength of filtering, which may result in either under-or over-filtering. Therefore, the adaptive window size estimation method proposed in a previous study [35] is applied in the process of principal phase component estimation.…”

Section: A Spatial Adaptive Filteringmentioning

confidence: 99%

An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

Zhang

et al. 2021

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

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The multi-temporal interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) has been widely applied in high-precision deformation measurements, which compensates for the drawback that the persistent scatterer InSAR technique does not obtain sufficient monitoring points, especially in rural areas. Considering that DS pixels are susceptible to various decorrelation factors, it is necessary to retrieve the optimal phase series by phase optimization algorithms (POAs). However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. To overcome this limitation, an adaptive POA is proposed in this paper. The adaptive POA artificially constructs a superior CCM by the filtered interferometric phase, which is derived through spatial adaptive filtering approach fusion of principal phase component estimation and fast nonlocal means filtering, and an accurate coherence matrix determined via coherence estimation bias correction. Moreover, the modified eigen-decomposition-based maximum-likelihood-estimator of the interferometric phase (EMI) with coherence-power-weighting is proposed to further improve the estimation precision and computational efficiency. The estimated CCM is then processed with the modified coherence-power-weighted EMI algorithm, and the optimal phase history is retrieved. The experimental results validated against both simulated and Sentinel-1A data demonstrate the superior optimization performance and robustness of the adaptive POA over traditional POAs.

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Fringe Estimation in Distributed Scatterer Interferometry

Yao,

Balz

2023

IEEE Trans. Geosci. Remote Sensing

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Slope‐compensated interferogram filter with ESPRIT for adaptive frequency estimation

Cited by 3 publications

References 18 publications

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

Review of interferometric synthetic aperture sonar interferometric phase filtering methods

An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

Fringe Estimation in Distributed Scatterer Interferometry

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