A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

Fang, Dongsheng; Lv, Xiaolei; Wang, Yong; Lin, Xue; Qian, Jiang

doi:10.3390/rs8100830

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…The quantitative indexes are the number of residues (NOR) [1] remaining after filtering, the MSE between the filtered phase and the corresponding ideal phase, the mean structural similarity index (MSSIM) [26] between the filtered phase and the corresponding ideal phase, and running time (T). Lacking the ideal interferometric phase, the no-reference metric Q [26,27] was used in the experiments on real data. The metric Q can provide a quantitative measure of the phase detail information.…”

Section: Performance Evaluationmentioning

confidence: 99%

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: Performance Evaluationmentioning

confidence: 99%

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

Zhou

et al. 2022

Remote Sensing

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“…For real InSAR data, the no-reference metric Q is adopted, because the clean/reference interferometric phase is usually unknown. The index can comprehensively reflect the capacities of noise suppression and detail preservation of the filtering method [16,49], so it is suitable for our evaluation. According to [49], the local dominant orientation is calculated by the singular value decomposition of local interferometric phase image gradient matrix G…”

Section: Nmentioning

confidence: 99%

“…Besides, the first wavelet domain method for interferometric phase filtering is proposed in [14], which reduces noise in the complex wavelet plane. This work has important implications for subsequent research [15,16]. Generally speaking, wavelet domain filters have a better capacity to preserve spatial resolution when compared with spatial domain methods.…”

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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“…10,11 WTs decompose the input signal into a series of distinctive frequencies that represent different characteristics of the signal and have the capability to reflect the nonstationarity of the signal. With the benefits of multiscale and multiresolution operation, WTs have been widely applied in many studies, including for hyperspectral image denoising, 12,13 compression, 14 classification, 15 and image fusion or enhancement. 16,17 Guo et al 18 employed a WT filter to the harmonic detection systems.…”

Section: Introductionmentioning

confidence: 99%

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters,"Abstract. Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove smallamplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat's biophysical and biochemical parameters. The results indicated that the CF removed both large-and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R 2 ) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R 2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

Cited by 8 publications

References 33 publications

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

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

A Phase Filtering Method with Scale Recurrent Networks for InSAR

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

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