A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Pu, Liming; Zhou, Zenan; Li, Liang; Zhou, Liming; Shi, Jun; Wei, Shunjun

doi:10.3390/rs13224564

Cited by 21 publications

(13 citation statements)

References 35 publications

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“…To enhance the generalization capability of NL-PFNet, we used a digital elevation model (DEM) to generate interferometric phase images with topography features, which can enhance the phase feature similarity between the simulated and real InSAR data [24,25]. According to the ambiguity height h 2π of the InSAR system and terrain height, the interferometric phase can be calculated by…”

Section: Data Generationmentioning

confidence: 99%

“…In addition, to increase the network's ability to handle different levels of noise, we generated interferometric phase images with different coherences according to the phase noise model described in Section 2.1, and the coherence range is in [0.5, 0.95] with an interval of 0.05. This range can cover most interferometric phase data in practical application [24] and avoid data with coherence ρ < 0.…”

Section: Data Generationmentioning

confidence: 99%

“…Furthermore, the ambiguity height of the InSAR system directly affects the density of phase fringes related to the filtering performance. Therefore, to enhance the phase feature similarity between simulated and real InSAR data [24,25], the ambiguity height used in the process of generating the training data is the same as that of the real InSAR system. Table 6.…”

Section: Generalization Ability To Real Insar Datamentioning

confidence: 99%

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Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Zhou

et al. 2022

Remote Sensing

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Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods.

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Section: Data Generationmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Generalization Ability To Real Insar Datamentioning

confidence: 99%

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Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Zhou

et al. 2022

Remote Sensing

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“…Path-following algorithms are difficult to deal with the integration-path inconsistency issue. Recently, deep learning methods have been widely exploited in PhU [16][17][18][19][20]. Such methods include encoder-decoder [16][17] architectures to achieve semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Convolutional deep learning network for InSAR phase denoising and unwrapping

Fejjari,

Valentino,

Briffa

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

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Interferometric phase unwrapping is one of the most challenging research topics for the remote sensing community. Recovering and correctly estimating the true interferometric phase signal from the received wrapped one provides critical information about changes in the Earth’s surface over time. Interferometric synthetic aperture radar (InSAR) has been widely used to extract such displacement estimates. However, InSAR images are affected often by a particular type of noise known as Gaussian. The presence of Gaussian noise in InSAR data can make the phase unwrapping process more difficult. In this paper, we introduce a convolutional deep learning-based network to perform simultaneous interferometric phase denoising and unwrapping. Quantitative and qualitative evaluations, made on synthetic and real world InSAR data, show that the proposed approach is able to produce accurate results even in the presence of strong noise.

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“…In SAR imaging or image field, each pixel is surrounded by other pixels, that is, the phase is continuous in space. Therefore, methods such as parameter estimation [12,13], clustering [14], extended minimum cost flow [15], and neural network [16][17][18] can be used for phase unwrapping. In the field of acoustic signal processing, because the signal carrier frequency is low and the relative speed of target and sensor is small, phase unwrapping can be realized through the continuity of phase in time [19,20].…”

Section: Introductionmentioning

confidence: 99%

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

Liu

et al. 2022

Remote Sensing

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Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.

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A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Cited by 21 publications

References 35 publications

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Convolutional deep learning network for InSAR phase denoising and unwrapping

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

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