DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

Sun, Xinyao; Zimmer, Aaron; Mukherjee, Subhayan; Kottayil, Navaneeth Kamballur; Ghuman, Parwant; Cheng, Irene

doi:10.3390/rs12142340

Cited by 33 publications

(39 citation statements)

References 59 publications

(95 reference statements)

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“…Our main focus is studying parameter estimation of linear deformation rate and DEM error upon PS time series, for the following reasons. (1) There are many state-of-the-art methods for filtering random noise and suppressing atmosphere components from a stack of interferograms [25,27,[32][33][34]. (2) Recently, satellite facilities can provide accurate enough orbits for practical usage [35][36][37].…”

Section: Proposed Methods 221 Definition Of Optimization Problemmentioning

confidence: 99%

“…During optimization, the optimizer adjusts variables in order to force the reconstructed phasor close to the target phasor based on the perspective of projections on two axes. This approach has been commonly deployed as a good indication of wrapped phase distances in recent InSAR phase filtering studies [24,25,41]. At this point, our signal separation task is formulated as a parameter fitting problem by optimizing an objective function.…”

Section: Proposed Methods 221 Definition Of Optimization Problemmentioning

confidence: 99%

“…The observed phase is also wrapped asφ l . A general PSI processing chain eventually removes the flatearth phase using satellite orbit data, and a collection of spatial and temporal filtering routines are adopted to reduce noise and remove other contaminant signals that are not deformation or DEM error [24][25][26]. Several established techniques are also described to mitigate the atmospheric phase contribution by deploying the toolbox for reducing atmospheric InSAR noise (TRAIN) [27,28].…”

Section: Mathematical Modelling For Insar Phasementioning

confidence: 99%

See 2 more Smart Citations

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Sun¹,

Zimmer²,

Mukherjee³

et al. 2021

Remote Sensing

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Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

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Section: Proposed Methods 221 Definition Of Optimization Problemmentioning

confidence: 99%

Section: Proposed Methods 221 Definition Of Optimization Problemmentioning

confidence: 99%

Section: Mathematical Modelling For Insar Phasementioning

confidence: 99%

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IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Sun¹,

Zimmer²,

Mukherjee³

et al. 2021

Remote Sensing

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“…apply deep learning for phase gradient estimation in two-dimensional phase unwrapping [17]. In other cases, deep learning is also used to detect volcano deformation, map forest, restore InSAR phases, and estimate DEM [20][21][22][23]. However, to the best knowledge of authors, the deep learning method has not been used to generate decorrelation masks, which is a simple but important procedure for InSAR.…”

Section: Introductionmentioning

confidence: 99%

Selective Kernel Res-Attention UNet: Deep Learning for Generating Decorrelation Mask With Applications to TanDEM-X Interferograms

Zhang¹,

Wang²,

Pei³

et al. 2021

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

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Decorrelation is one of the main limitations for InSAR. Masking decorrelated pixels is crucial for retrieving information from SAR interferograms. However, for traditional masking methods, manually drawing masks is time-consuming and may be unfeasible when decorrelation areas are with complicated and blurred boundaries. Setting a single coherence threshold is also difficult, if not impossible, to mask out all decorrelated pixels without losing valid phases. Here, we propose a deep-learning segmentation network (Mask Net) based on Selective Kernel Res-Attention UNet, for generating decorrelation masks with applications to TanDEM-X interferograms. We conduct several experiments to determine the training strategy and parameters, including sample size, batch size, loss function and down-sampling scheme, to optimize network performance. Afterwards, we compare the performance of Mask Net with other classical segmentation networks. Our evaluation metrics show that Mask Net outperforms the best performance of other segmentation networks by IoU of 6.32% and F1 Score of 3.97%, respectively. It also possesses the fastest inferring speed, 0.4505s on sample size of 1024-by-1024 pixels, which is at least ~50% faster than other segmentation networks. We applied Mask Net to three TanDEM-X interferograms of Kīlauea crater in Hawaii, metropolitan region of Wuhan, and Muztagata Glacier in China. Our results show that comparing with coherence threshold method, Mask Net can clearly mask out all decorrelation regions, rarely causing loss of valid phases. It also exhibits better segmentation performance than other deep-learning segmentation networks, especially for those complex decorrelation boundaries, with less computational time.

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“…In recent years, deep learning has been successfully applied to many fields, such as machine vision, optical image processing [27][28][29][30], and SAR image denoising [31][32][33]. In addition, the research of deep learning in the field of interferometric SAR has also begun to sprout [34][35][36]. In this paper, we propose a deep learning-based method to filter the interferometric phase for InSAR, which can better balance the noise suppression capacity and phase detail preservation capacity in order to obtain higher-precision results and ensure computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Phase Filtering Method with Scale Recurrent Networks for InSAR

Zhang

Zhou

et al. 2020

Remote Sensing

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Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications, such as deformation monitoring and topographic mapping. The accuracy of the deformation and terrain height is highly dependent on the quality of phase filtering. Researchers are committed to continuously improving the accuracy and efficiency of phase filtering. Inspired by the successful application of neural networks in SAR image denoising, in this paper we propose a phase filtering method that is based on deep learning to efficiently filter out the noise in the interferometric phase. In this method, the real and imaginary parts of the interferometric phase are filtered while using a scale recurrent network, which includes three single scale subnetworks based on the encoder-decoder architecture. The network can utilize the global structural phase information contained in the different-scaled feature maps, because RNN units are used to connect the three different-scaled subnetworks and transmit current state information among different subnetworks. The encoder part is used for extracting the phase features, and the decoder part restores detailed information from the encoded feature maps and makes the size of the output image the same as that of the input image. Experiments on simulated and real InSAR data prove that the proposed method is superior to three widely-used phase filtering methods by qualitative and quantitative comparisons. In addition, on the same simulated data set, the overall performance of the proposed method is better than another deep learning-based method (DeepInSAR). The runtime of the proposed method is only about 0.043s for an image with a size of 1024×1024 pixels, which has the significant advantage of computational efficiency in practical applications that require real-time processing.

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DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

Cited by 33 publications

References 59 publications

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Selective Kernel Res-Attention UNet: Deep Learning for Generating Decorrelation Mask With Applications to TanDEM-X Interferograms

A Phase Filtering Method with Scale Recurrent Networks for InSAR

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