InSAR Phase Denoising: A Review of Current Technologies and Future Directions

Xu, Gang; Gao, Yandong; Li, Jinwei; Xing, Mengdao

doi:10.1109/mgrs.2019.2955120

Cited by 78 publications

(21 citation statements)

References 106 publications

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“…The Doppler ambiguities do not occur since the cross-track velocities of moving targets are less than 25 m/s, which is the maximum baseband velocity. Figure 8a,b demonstrates the clutter and target spectra before and after clutter suppression, respectively, where clutter suppression is realized by using the EFA method [43][44][45]. From these figures, after clutter reduction, the moving targets are visible, but they are blurred because of the RCM and DFM.…”

Section: Measured Data Results and Analysismentioning

confidence: 99%

An Efficient Ground Manoeuvring Target Refocusing Method Based on Principal Component Analysis and Motion Parameter Estimation

Liu

et al. 2020

Remote Sensing

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Refocusing ground manoeuvring targets with complex motions in synthetic aperture radar (SAR) remains a challenging objective because of the large range of cell migration (RCM) and time-varying Doppler frequency modulation (DFM). By exploiting the geometric information of RCM and two-dimensional (2-D) coherently integrated gain, a fast ground manoeuvring target refocusing method using principal component analysis (PCA) and high-order motion parameter estimation is proposed. First, an efficient phase difference (PD) method and PCA are utilized to correct the RCM, and then, the energy of the ground manoeuvring target is concentrated into the same range bin. Second, by utilizing the coherently integrated cubic phase function (CICPF) that was developed in our previous work, the motion parameters are obtained accurately, and the manoeuvring target is thus well refocused into a sharp peak point based on the estimated motion parameters. The proposed method is of low computational complexity because it avoids time-consuming search and interpolation operations and demonstrates an improved anti-noise performance due to fully exploiting the 2-D coherent accumulation characteristics for estimating motion parameters and enhanced refocused imaging results for manoeuvring targets due to adopting the high-order motion model. Finally, experiments are conducted using simulated and real SAR data to show the performance of the proposed method.

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Section: Measured Data Results and Analysismentioning

confidence: 99%

An Efficient Ground Manoeuvring Target Refocusing Method Based on Principal Component Analysis and Motion Parameter Estimation

Liu

et al. 2020

Remote Sensing

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“…Interferometric synthetic aperture radar (InSAR), as we all know, is an advanced remote sensing method in surface measurement (Xu et al., 2020). Compared with the traditional surface measurement methods, it has the characteristics of high precision, wide coverage and basically unaffected by weather conditions (Ramirez et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The fundamental InSAR technique is to interfere two SAR images to obtain an interferogram (Moreira et al., 2013), which has the difference in phase between the two images from almost the same places (Xu et al., 2020). “Interfere” here means conjugate multiplication of the SLC (single look complex) data obtained by SAR satellite containing amplitude and phase information.…”

Section: Introductionmentioning

confidence: 99%

A Fast Non‐Local Means Filtering Method for Interferometric Phase Based on Wavelet Packet Transform

2021

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Phase unwrapping is very important to acquire surface deformation using InSAR, unwrapping the interferometric phase directly is not appropriate because the phase is affected by various noises. Therefore, it is essential to apply a suitable filter to the interferometric phase before phase unwrapping. Although there are many filtering methods for noise, these methods cannot be used directly due to the particularity of interferometric phase noise and therefore it is important to do the corresponding domain transformation. Here, a fast non‐local means filtering method based on wavelet packet transform is proposed. Originally, wavelet packet transform is performed on the real part and the imaginary part respectively, which avoids the influence of phase jumps on the subsequent filtering. Furthermore, the fast non‐local means filtering can be applied to smooth the acquired wavelet packet coefficients. Eventually, inverse transform of the wavelet packet is used to reconstruct the phase after filtering. Compared with other filtering algorithms in simulated and actual Sentinel‐1 data, the superiority of this algorithm is proved.

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“…Different from the spaceborne synthetic aperture radar (SAR) [1][2][3][4][5][6], airborne SAR is frequently affected by atmospheric turbulence, and thus, its flight trajectory may deviate from a pre-planned straight-line trajectory [7][8][9][10]. Therefore, combining motion compensation (MoCo)/autofocus processing for airborne SAR imaging [11][12][13][14] is necessary.…”

Section: Introductionmentioning

confidence: 99%

Airborne SAR Autofocus Based on Blurry Imagery Classification

Chen

et al. 2021

Remote Sensing

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Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all types of scenes, in theory, but their efficiency is generally low. In practice, whether many dominant point targets are present in the scene is usually unknown, so determining what kind of algorithm should be selected is not straightforward. To solve this issue, this article proposes an airborne SAR autofocus approach combined with blurry imagery classification to improve the autofocus efficiency for ensuring autofocus precision. In this approach, we embed the blurry imagery classification based on a typical VGGNet in a deep learning community into the traditional autofocus framework as a preprocessing step before autofocus processing to analyze whether dominant point targets are present in the scene. If many dominant point targets are present in the scene, the non-parametric method is used for autofocus processing. Otherwise, the parametric one is adopted. Therefore, the advantage of the proposed approach is the automatic batch processing of all kinds of airborne measured data.

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InSAR Phase Denoising: A Review of Current Technologies and Future Directions

Cited by 78 publications

References 106 publications

An Efficient Ground Manoeuvring Target Refocusing Method Based on Principal Component Analysis and Motion Parameter Estimation

An Efficient Ground Manoeuvring Target Refocusing Method Based on Principal Component Analysis and Motion Parameter Estimation

A Fast Non‐Local Means Filtering Method for Interferometric Phase Based on Wavelet Packet Transform

Airborne SAR Autofocus Based on Blurry Imagery Classification

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