41 results on '"Phase filtering"'
Search Results
2. CBIPDNet: A Novel Method for InSAR Deformation Interferometric Phase Filtering Using Deep Learning Network
- Author
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Yandong Gao, Jiaqi Yao, Wei Zhou, Nanshan Zheng, Shijin Li, and Yu Tian
- Subjects
Convolutional blind denoising network ,differential interferometric synthetic aperture radar (DInSAR) ,phase filtering ,subsidence deformation ,Ocean engineering ,TC1501-1800 ,Geophysics. Cosmic physics ,QC801-809 - Abstract
The denoising of phase is a crucial process that impacts the accuracy of data processing in differential interferometric synthetic aperture radar. Especially in the area of large-gradient deformation, the phase filtering method is very easy to cause phase losses. This has a significant impact on the final deformation acquisition. To address this issue, here, a deep convolutional blind denoising network-based interferometric phase filtering method, named CBIPDNet, is proposed. Different from the previously proposed deep learning phase filtering methods, CBIPDNet does not add noise to the input before filtering, but adds noise to the input during the training process. Furthermore, CBIPDNet uses a CNN structure for adaptive noise estimation and uses a residual module for nonblind filtering. Therefore, CBIPDNet can be considered as an adaptive phase filtering algorithm. More importantly, the added noise is composed of heteroscedastic Gaussian noise + simulated real noise of the imaging process, which is closer to the real interferometric noise phase. Moreover, the denoising effect of targets of different scales through the asymmetric loss function has been significantly improved, which can improve the detail preservation ability of regions with substantial gradient deformations. The experimental results demonstrate that CBIPDNet is capable of enhancing phase quality and increasing phase unwrapping accuracy compared to the current interferometric filtering methods.
- Published
- 2024
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3. Effective Denoising of InSAR Phase Images via Compressive Sensing
- Author
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Min-Seok Kang and Jae-Min Baek
- Subjects
Compressive sensing (CS) ,interferometric synthetic aperture radar (InSAR) ,phase filtering ,sparse signal processing ,synthetic aperture radar (SAR) ,Ocean engineering ,TC1501-1800 ,Geophysics. Cosmic physics ,QC801-809 - Abstract
Interferometric synthetic aperture radar (InSAR) denoising is an essential processing step in deformation measurement and topography reconstruction. A noisy InSAR phase image gives rise to the phase unwrapping difficulties and even results in the degradation of various final products of InSAR. To address this issue, we develop a compressive sensing (CS)-based InSAR phase denoising technique in this article. Since the spectrum of the InSAR phase image is usually sparse in the 2-D frequency domain, the estimation of sensing dictionary matrix of the linear system between the InSAR phase signal and its spectrum in the pursuit of sparsity is considered for InSAR phase denoising. The optimization problem derived by the signal parameterization approach is effectively carried out by estimating the basis function that is closely analogous to the strongest signal component in the spectrum of the InSAR phase image. The proposed method is effectively capable of eliminating noise and preserving detailed fringe information of InSAR. In the end, simulations and experimental results demonstrate that the proposed scheme outperforms other conventional InSAR phase denoising methods.
- Published
- 2024
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4. Enhanced Goldstein Filter for Interferometric Phase Denoising Using 2-D Variational Mode Decomposition
- Author
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Rahul Dasharath Gavas, Soumya Kanti Ghosh, and Arpan Pal
- Subjects
2-D variational mode decomposition (2D-VMD) ,phase filtering ,radar interferometry ,synthetic aperture radar (SAR) ,Instruments and machines ,QA71-90 ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Denoising of interferograms is a vital step in the processing of synthetic aperture radar (InSAR) data. The primary goal is to filter the noise to the extent possible while retaining the fringes of the interferograms. Among the widely available classes of filters, the frequency-domain filters are still being used, owing to their robustness and generalizability to varying phase noise characteristics. This article deals with an enhancement to the well-known frequency-domain filter, i.e., the Goldstein filter, which is basically a phase filtering algorithm for interferometric products. The proposed extension to the Goldstein filter deals with deriving the tuning parameter based on the spatial frequency modes. This is achieved by using the mode-level characteristics rendered by the 2-D version of variational mode decomposition (2D-VMD) on the interferograms under test. The results of simulation and real interferogram data show that the proposed approach reduces the noise levels while minimizing the loss of signal.
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- 2023
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5. Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping.
- Author
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Murdaca, Gianluca, Rucci, Alessio, and Prati, Claudio
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DEEP learning , *ARTIFICIAL neural networks , *SYNTHETIC aperture radar , *CONVOLUTIONAL neural networks , *SIGNAL processing , *PHASE noise - Abstract
Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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6. Transverse Intensity Distribution on the Far-Field Plane of Azimuthal Walsh Filters
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Bhattacharya, Indrani, Hazra, Lakshminarayan, Atai, Javid, Series Editor, Liang, Rongguang, Series Editor, Dinish, U.S., Series Editor, Bhattacharya, Indrani, and Hazra, Lakshminarayan
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- 2020
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7. Evaluation of Several Filtering and Unwrapping Methods for the Interferometric Imaging Radar Altimeter
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Tan, Hong, Li, Shengyang, Liu, Zhiwen, Zhang, Wanfeng, Li, Leijuan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Gu, Yidong, editor, Gao, Ming, editor, and Zhao, Guangheng, editor
- Published
- 2019
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8. A Fast Non‐Local Means Filtering Method for Interferometric Phase Based on Wavelet Packet Transform.
- Author
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Yan, Zhan, Yan, Hang, and Wang, Tao
- Subjects
INTERFEROMETRY ,SYNTHETIC aperture radar ,IMAGING systems ,REMOTE sensing ,COHERENT radar - Abstract
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. Key Points: Phase filtering is very important to the results of interferometry, and a good filter can make the results more accurateWavelet packet transform and fast non‐local means filter are combined to filtering the noisy phase signal, and its effectiveness is verified by comparing with various filters [ABSTRACT FROM AUTHOR]
- Published
- 2021
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9. A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
- Author
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Yanan You, Rui Wang, and Wenli Zhou
- Subjects
Synthetic aperture radar (SAR) ,SAR interferometry (InSAR) ,tensor decomposition ,KBR ,phase filtering ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Multi-pass synthetic aperture radar interferometry (InSAR) stack data denoising is a significant prerequisite for extracting geophysical parameters. InSAR stack data can be considered as a third-order tensor in the complex domain, and the process of tensor decomposition to acquire the low-rank tensor has been employed as an effective interferometric phase filter for InSAR stack data. It is noted that the definition of tensor rank is the core of tensor-based filter. In this paper, we investigate the properties of Tucker rank, CANDECAMP/PARAFAC (CP) rank and Kronecker Basis Representation (KBR) in InSAR stack data, and then we found that it is suitable to extend KBR, as a hybrid tensor rank representation, into InSAR tensor filtering. Firstly, an improved InSAR phase tensor model is utilized to represent the phenomenon of interferometric phase, which perceives the observed InSAR phase tensor as the combination of low-rank, sparse noise and Gaussian noise tensors. Based on the principle of KBR, then the novel phase filtering method, named as KBR-InSAR, is proposed to decompose the complex InSAR tensor supported by the improved InSAR phase tensor model. With the comparison of other tensor filters, i.e. HoRPCA and WHoRPCA and the widespread traditional filters operating on a single interferometric pair, e.g. Goldstein, NL-SAR, NL-InSAR and InSAR-BM3D, it can be proved that the KBR-InSAR can efficiently reduce the noise with superior fringes preservation in the experiments on the simulated and real InSAR stack data collected from Sentinel-1B.
- Published
- 2019
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10. A Closed-Form Robust Cluster-Analysis-Based Multibaseline InSAR Phase Unwrapping and Filtering Algorithm With Optimal Baseline Combination Analysis.
- Author
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Yuan, Zhihui, Lu, Zhong, Chen, Lifu, and Xing, Xuemin
- Subjects
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ALGORITHMS , *IMAGE reconstruction algorithms , *SYNTHETIC aperture radar , *CLUSTER analysis (Statistics) - Abstract
Phase unwrapping (PU) and phase filtering are the key procedures for the interferometric synthetic aperture radar (InSAR) technology. As one of the most popular multibaseline PU (MBPU) algorithms, the cluster-analysis (CA)-based MBPU algorithm still has some problems that need to be improved. To begin with, the cluster ambiguity vector is obtained by searching the nearest integer point to the cluster centerline with known slope and intercept in the search space. It will be time-consuming and inconvenient when the number of baselines or the search space is too large. In addition, they do not have the capacity of phase filtering. Moreover, they do not consider the impact of different baseline combinations on the performance of the CA-based MBPU algorithm. For these reasons, a novel CA-based MBPU and filtering (MBPUF) algorithm is proposed in this article. The main contributions of this article are that it gives the closed-form solving formulas of the cluster ambiguity vector to improve the efficiency of the CA-based MBPU algorithm, proposes a novel MB InSAR phase-filtering strategy that makes the CA-based MBPU algorithm capable of solving the phase-discontinuity problem and improving the height-reconstruction accuracy simultaneously, and utilizes the optimal baseline combination to improve the robustness of the CA-based MBPU algorithm. Theoretical analysis and experiments on both simulated and real MB InSAR data sets show the effectiveness and robustness of the proposed closed-form robust CA-based MBPUF algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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11. A Phase Filtering Method with Scale Recurrent Networks for InSAR
- Author
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Liming Pu, Xiaoling Zhang, Zenan Zhou, Jun Shi, Shunjun Wei, and Yuanyuan Zhou
- Subjects
interferometric synthetic aperture radar ,scale recurrent network ,phase filtering ,Science - Abstract
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.
- Published
- 2020
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12. An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition
- Author
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Yanan You, Rui Wang, and Wenli Zhou
- Subjects
multi-pass SAR interferometry (InSAR) ,data fusion ,online tensor decomposition ,phase filtering ,Science - Abstract
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio.
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- 2020
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13. Modified human contrast sensitivity function based phase mask for susceptibility-weighted imaging
- Author
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Wei-Hsin Wang, David C. Reutens, Zhengyi Yang, Giang Nguyen, and Viktor Vegh
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Susceptibility weighted imaging ,Phase mask ,Phase filtering ,Human contrast sensitivity function ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
The aim of the work is to increase the visual information in magnetic resonance imaging based susceptibility weighted images. Our approach is to amplify spatial frequency information of the phase mask used to increase susceptibility weighting using a modified version of the human contrast sensitivity function. Thereby, we propose a modified version of the human contrast sensitivity function for use in phase mask creation. Comparison with conventional susceptibility-weighted imaging was undertaken on a qualitative basis and quantitatively with a number of established image quality metrics on ex vivo mouse brain magnetic resonance images obtained at 16.4 T at various echo times. Four experts also compared the quality of in vivo 1.5 and 3 T human brain magnetic resonance images generated with traditional susceptibility weighted imaging and with the new method. We found that parameters of the modified human contrast sensitivity function can be chosen to improve delineation of structural detail of mouse and human brains. Information contained in susceptibility-weighted images generated using the modified human contrast sensitivity function based phase mask corresponds to that in the conventional method, however the visual range over which it is depicted has improved visual perception. Hence, qualitative evaluation of information contained in susceptibility-weighted images can be improved by amplifying spatial frequencies where human contrast sensitivity is reduced.
- Published
- 2014
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14. 多频干涉SAR局部条纹频率估计方法.
- Author
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林 圣, 王 震, 丁泽刚, 刘天冬, and 曾 涛
- Abstract
Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2017
15. SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics.
- Author
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Yonghong He, Jianjun Zhu, Haiqiang Fu, Qinghua Xie, Bing Xu, and Bing Zhang
- Subjects
SYNTHETIC aperture radar ,DIGITAL elevation models ,SPATIAL filters - Abstract
This paper presents a new filtering approach for Synthetic Aperture Radar (SAR) interferometric phase noise reduction in the shearlet domain, depending on the coherent statistical characteristics. Shearlets provide a multidirectional and multiscale decomposition that have advantages over wavelet filtering methods when dealing with noisy phase fringes. Phase noise in SAR interferograms is directly related to the interferometric coherence and the look number of the interferogram. Therefore, an optimal interferogram filter should incorporate information from both of them. The proposed method combines the phase noise standard deviation with the shearlet transform. Experimental results show that the proposed method can reduce the interferogram noise while maintaining the spatial resolution, especially in areas with low coherence. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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16. Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping
- Author
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Gianluca Murdaca, Alessio Rucci, and Claudio Prati
- Subjects
deep learning ,interferometric synthetic aperture radar (InSAR) ,phase filtering ,coherence estimation ,General Earth and Planetary Sciences - Abstract
Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones.
- Published
- 2022
17. A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage.
- Author
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Xiaolei Lv, Dongsheng Fang, Yong Wang, Xue Lin, and Jiang Qian
- Subjects
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INTERFEROMETRY , *SYNTHETIC aperture radar , *ALGORITHMS , *BAYESIAN analysis , *COEFFICIENTS (Statistics) , *ROOT-mean-squares - Abstract
An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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18. Modified patch-based locally optimal Wiener method for interferometric SAR phase filtering.
- Author
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Wang, Yang, Huang, Haifeng, Dong, Zhen, and Wu, Manqing
- Subjects
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SYNTHETIC aperture radar , *MEAN square algorithms , *LINEAR statistical models , *GAUSSIAN processes , *ESTIMATION theory , *ADAPTIVE estimation (Statistics) - Abstract
This paper presents a modified patch-based locally optimal Wiener (PLOW) method for interferometric synthetic aperture radar (InSAR) phase filtering. PLOW is a linear minimum mean squared error (LMMSE) estimator based on a Gaussian additive noise condition. It jointly estimates moments, including mean and covariance, using a non-local technique. By using similarities between image patches, this method can effectively filter noise while preserving details. When applied to InSAR phase filtering, three modifications are proposed based on spatial variant noise. First, pixels are adaptively clustered according to their coherence magnitudes. Second, rather than a global estimator, a locally adaptive estimator is used to estimate noise covariance. Third, using the coherence magnitudes as weights, the mean of each cluster is estimated, using a weighted mean to further reduce noise. The performance of the proposed method is experimentally verified using simulated and real data. The results of our study demonstrate that the proposed method is on par or better than the non-local interferometric SAR (NL-InSAR) method. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
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19. Développements algorithmiques pour l’amélioration des résultats de l’interférométrie RADAR en milieu urbain
- Author
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Tlili, Ayoub, Cavayas, François, and Foucher, Samuel
- Subjects
Filtrage de phase ,InSAR ,Phase filtering ,MNA ,Phase unwrapping ,DEM ,Déroulement de phase ,DInSAR ,Surface displacement maps ,Cartes de mouvement de surface - Abstract
Le suivi des espaces urbanisés et de leurs dynamiques spatio-temporelles représente un enjeu important pour la population urbaine, autant sur le plan environnemental, économique et social. Avec le lancement des satellites portant des radars à synthèse d’ouverture de la nouvelle génération (TerraSAR-X, COSMO-SkyMed, ALOS, RADARSAT-2,Sentinel-1, Constellation RADARSAT), il est possible d’obtenir des séries temporelles d’images avec des résolutions spatiales et temporelles fines. Ces données multitemporelles aident à mieux analyser et décrire les structures urbaines et leurs variations dans l’espace et dans le temps. L’interférométrie par satellite est effectuée en comparant les phases des images RSO prises à différents passages du satellite au-dessus du même territoire. En optant pour des positions du satellite séparées d’une longue ligne de base, l’InSAR mène à la création des modèles numériques d’altitude (MNA). Si cette ligne de base est courte et à la limite nulle, nous avons le cas de l’interférométrie différentielle (DInSAR) qui mène à l’estimation du mouvement possible du terrain entre les deux acquisitions. Pour toutes les deux applications de l’InSAR, deux opérations sont importantes qui garantissent la génération des interférogrammes de qualité. La première est le filtrage du bruit omniprésent dans les phases interférométriques et la deuxième est le déroulement des phases. Ces deux opérations deviennent particulièrement complexes en milieu urbain où au bruit des phases s’ajoutent des fréquents sauts et discontinuités des phases dus à la présence des bâtiments et d’autres structures surélevées. L’objectif de cette recherche est le développement des nouveaux algorithmes de filtrage et de déroulement de phase qui puissent mieux performer que les algorithmes considérés comme référence dans ce domaine. Le but est d’arriver à générer des produits InSAR de qualité en milieu urbain. Concernant le filtrage, nous avons établi un algorithme qui est une nouvelle formulation du filtre Gaussien anisotrope adaptatif. Quant à l’algorithme de déroulement de phase, il est fondé sur la minimisation de l’énergie par un algorithme génétique ayant recours à une modélisation contextuelle du champ de phase. Différents tests ont été effectués avec des images RSO simulées et réelles qui démontrent le potentiel de nos algorithmes qui dépasse à maints égards celui des algorithmes standard. Enfin, pour atteindre le but de notre recherche, nous avons intégré nos algorithmes dans l’environnement du logiciel SNAP et appliqué l’ensemble de la procédure pour générer un MNA avec des images RADARSAT-2 de haute résolution d’un secteur de la Ville de Montréal (Canada) ainsi que des cartes des mouvements du terrain dans la région de la Ville de Mexico (Mexique) avec des images de Sentinel-1 de résolution plutôt moyenne. La comparaison des résultats obtenus avec des données provenant des sources externes de qualité a aussi démontré le fort potentiel de nos algorithmes., The monitoring of urban areas and their spatiotemporal dynamics is an important issue for the urban population, at the environmental, economic, as well as social level. With the launch of satellites carrying next-generation synthetic aperture radars (TerraSAR-X, COSMO-SkyMed, ALOS, RADARSAT-2, Sentinel-1, Constellation RADARSAT), it is possible to obtain time series of images with fine temporal and spatial resolutions. These multitemporal data help to better analyze and describe urban structures, and their variations in space and time. Satellite interferometry is performed by comparing the phases of SAR images taken at different satellite passes over the same territory. By opt-ing for satellite positions separated by a long baseline, InSAR leads to the creation of digital elevation models (DEM). If this baseline is short and, at the limit zero, we have the case of differential interferometry (DInSAR) which leads to the estimation of the possible movement of the land between the two acquisitions. In both InSAR applica-tions, two operations are important that ensure the generation of quality interferograms. The first is the filtering of ubiquitous noise in the interferometric phases and the second is the unwrapping of the phases. These two operations become particularly complex in urban areas where the phase noise is added to the frequent jumps and discontinuities of phases due to the presence of buildings and other raised structures. The objective of this research is the development of new filtering and phase unwrap-ping algorithms that can perform better than algorithms considered as reference in this field. The goal is to generate quality InSAR products in urban areas. Regarding filtering, we have established an algorithm that is a new formulation of the adaptive anisotropic Gaussian filter. As for the phase unwrapping algorithm, it is based on the minimization of energy by a genetic algorithm using contextual modelling of the phase field. Various tests have been carried out with simulated and real SAR images that demonstrated the potential of our algorithms that in many respects exceeds that of standard algorithms. Finally, to achieve the goal of our research, we integrated our algorithms into the SNAP software environment and applied the entire procedure to generate a DEM with high-resolution RADARSAT-2 images from an area of the City of Montreal (Canada) as well as maps of land movement in the Mexico City region (Mexico) with relatively medium-resolution Sentinel-1 images. Comparison of the results with data from external quality sources also demonstrated the strong potential of our algorithms.
- Published
- 2021
20. Local frequency adaptive filtering method based on SNR for InSAR interferogram.
- Author
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Man Yan and Lifen Wang
- Subjects
- *
SYNTHETIC aperture radar , *ADAPTIVE filters , *SIGNAL-to-noise ratio , *INTERFEROMETRY , *SIGNAL frequency estimation - Abstract
Phase filtering is one of the key steps in interferometric synthetic aperture radar; its precision will directly lead to the phase unwrapping quality, thereby affect generating DEM reliability. For original algorithm causes inaccurate result in unevenly distributed noise region, so local frequency adaptive filtering method based on SNR is proposed. The local SNR is based on frequency estimation window size, and variable window is used to filter phase, this can make the areas with low SNR strongly filter, while those with high SNR weakly filter. Using simulation and real data to do experiments, results show that this paper method not only can effectively reduce noise, but also can better maintain detail stripes. [ABSTRACT FROM AUTHOR]
- Published
- 2015
21. A Phase Filter for Multi-Pass InSAR Stack Data by Hybrid Tensor Rank Representation
- Author
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Wenli Zhou, Yanan You, and Rui Wang
- Subjects
010504 meteorology & atmospheric sciences ,General Computer Science ,Rank (linear algebra) ,Computer science ,Noise reduction ,phase filtering ,0211 other engineering and technologies ,02 engineering and technology ,01 natural sciences ,symbols.namesake ,tensor decomposition ,Kronecker delta ,KBR ,Interferometric synthetic aperture radar ,General Materials Science ,Tensor ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Noise (signal processing) ,General Engineering ,Filter (signal processing) ,Synthetic aperture radar (SAR) ,Interferometry ,Gaussian noise ,SAR interferometry (InSAR) ,symbols ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,lcsh:TK1-9971 ,Algorithm - Abstract
Multi-pass synthetic aperture radar interferometry (InSAR) stack data denoising is a significant prerequisite for extracting geophysical parameters. InSAR stack data can be considered as a third-order tensor in the complex domain, and the process of tensor decomposition to acquire the low-rank tensor has been employed as an effective interferometric phase filter for InSAR stack data. It is noted that the definition of tensor rank is the core of tensor-based filter. In this paper, we investigate the properties of Tucker rank, CANDECAMP/PARAFAC (CP) rank and Kronecker Basis Representation (KBR) in InSAR stack data, and then we found that it is suitable to extend KBR, as a hybrid tensor rank representation, into InSAR tensor filtering. Firstly, an improved InSAR phase tensor model is utilized to represent the phenomenon of interferometric phase, which perceives the observed InSAR phase tensor as the combination of low-rank, sparse noise and Gaussian noise tensors. Based on the principle of KBR, then the novel phase filtering method, named as KBR-InSAR, is proposed to decompose the complex InSAR tensor supported by the improved InSAR phase tensor model. With the comparison of other tensor filters, i.e. HoRPCA and WHoRPCA and the widespread traditional filters operating on a single interferometric pair, e.g. Goldstein, NL-SAR, NL-InSAR and InSAR-BM3D, it can be proved that the KBR-InSAR can efficiently reduce the noise with superior fringes preservation in the experiments on the simulated and real InSAR stack data collected from Sentinel-1B.
- Published
- 2019
- Full Text
- View/download PDF
22. InSAR Phase Noise Reduction Based on Empirical Mode Decomposition.
- Author
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Li, Fangfang, Hu, Donghui, Ding, Chibiao, and Zhang, Wenyi
- Abstract
A novel method of interferometric synthetic aperture radar phase filtering that combines empirical mode decomposition (EMD) with Hölder exponent adjustment is presented in this letter. First, intrinsic mode functions (IMFs) of different levels are obtained by decomposing the real and imaginary parts of the noisy interferometric phase in complex formulation respectively employing EMD, which is a totally data-driven method without parameters to be selected. Then, we increase the Hölder exponents of every IMF to appropriate extent according to the features of the signal and noise contained in them to realize different filtering effects. Thus, noise can be efficiently filtered without the loss of detailed information of the interferogram. Finally, the filtered IMFs are reconstructed to form the denoised interferogram. The experiments of simulated data with various correlation coefficients and real data verify the effectiveness and adaptability of the method. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
- View/download PDF
23. Directionally Adaptive Filter for Synthetic Aperture Radar Interferometric Phase Images.
- Author
-
Sihua Fu, Xuejun Long, Xia Yang, and Qifeng Yu
- Subjects
- *
SYNTHETIC aperture radar , *ADAPTIVE filters , *INTERFEROMETRY , *COHERENT radar , *ELECTRIC filters - Abstract
The obvious directionality inherent in interferometric synthetic aperture radar interferograms allows noise to be filtered very effectively along the fringe direction, leaving the fringe phase undamaged. This paper proposes a highly reliable method for the simultaneous estimation of the direction and density of interferograms, on the basis of which a directionally adaptive filter (DAF) is further proposed. Compared with existing filters, the DAF has a filter window whose direction is able to vary continuously with the fringe direction. The window length and width can be varied adaptively with the fringe density: a large window achieves better filtering results when the fringes are sparse, whereas a small window is better able to preserve the phase detail when they are dense. The processing results of both simulated and real data demonstrate that the DAF effectively eliminates noise and preserves detailed fringe information because its filter window performs adaptively in terms of both direction and size. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
- View/download PDF
24. Susceptibility phase imaging with comparison to R2* mapping of iron-rich deep grey matter
- Author
-
Walsh, Andrew J. and Wilman, Alan H.
- Subjects
- *
MAGNETIC resonance imaging of the brain , *PERIAQUEDUCTAL gray matter , *BRAIN mapping , *IRON in the body , *SUBSTANTIA nigra , *SIMULATION methods & models , *RED nucleus - Abstract
Abstract: Magnetic resonance imaging with susceptibility phase is seeing increasing use, especially at high magnetic fields. Tissue susceptibility can produce unique phase contrast for qualitative or quantitative imaging of iron-rich deep grey matter. However, phase imaging has several established sources of error including inherent susceptibility field effects and artifacts from background phase removal. These artifacts have led to inconsistent findings in past works relating iron to phase in healthy deep grey matter. This study seeks to determine the relative artifactual contributions from inherent susceptibility fields and from high pass phase filtering, currently the most common and accessible background phase removal method. In simulation, phase is compared to a known susceptibility distribution, while R2* maps are used as the in vivo gold standard surrogate for iron in healthy volunteers. The results indicate phase imaging depends highly on filtering, structure size, shape and local environment. Using in vivo phase and R2* profiles, it is shown that different filtering values, commonly seen in the literature, can lead to substantially different phase measures. Correlations between phase and R2* mapping are shown to be highly variable between structures. For example, using a standard filter of 0.125 the slopes and correlation coefficients were 4.28×10−4 ppm*s and R =0.88 for the putamen, 0.81×10−4 ppm*s and R =0.08 for the globus pallidus, 5.48×10−4 ppm*s and R =0.72 for the red nucleus, and −14.64×10−4 ppm*s and R =0.54 for the substantia nigra. To achieve the most effective correlation to R2* we recommend using a filter width of 0.094 for the globus pallidus and putamen and 0.125 for the substantia nigra and red nucleus. The baseline phase measure should be obtained directly adjacent to the substantia nigra, and red nucleus to yield the most accurate phase values as demonstrated in simulation and in vivo. Different regression slopes are seen between subROIs within structures suggesting that regional iron accumulation within a structure is best studied with subROIs between different subject groups, not differences in phase values relative to the overall phase in one structure. Phase imaging with the standard high pass filter method has the potential to differentiate subtle iron changes in pathological processes compared to normal tissues with more reliability if specific filter strengths and measurement areas are appropriately applied on a structure dependent basis. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
25. Interferometric SAR Phase Filtering in the Wavelet Domain Using Simultaneous Detection and Estimation.
- Author
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Bian, Yong and Mercer, Bryan
- Subjects
- *
INTERFEROMETRY , *SYNTHETIC aperture radar , *DETECTORS , *ESTIMATION theory , *LEAST squares , *NOISE measurement , *WAVELETS (Mathematics) - Abstract
In this paper, two interferometric SAR (InSAR) phase-filtering methods are proposed. These methods are performed in the wavelet domain and employ the simultaneous detection and estimation technique. In the wavelet domain, closed-form estimator and detector equations are derived, based upon a quadratic cost function, to minimize the combined risk of detection and estimation and, thus, the least square errors. Both methods occur within the wavelet domain; however, the first method employs the wavelet packet, while the second method is performed in the undecimated wavelet domain. A major characteristic of InSAR phase data is that the noise level is spatially variable, and the proposed methods have a particularly good comparative performance in these situations. Tests are performed using simulated phase data and show that the proposed methods have lower root-mean-square error and less noisy fringes in the filtering results than those of three existing “state-of-the-art” wavelet-domain phase-filtering methods. Tests using real InSAR data also demonstrate the superiority of the proposed methods in terms of visual and quantitative evaluation. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
26. Improved filtering parameter determination for the Goldstein radar interferogram filter
- Author
-
Li, Z.W., Ding, X.L., Huang, C., Zhu, J.J., and Chen, Y.L.
- Subjects
- *
INTERFEROMETRY , *SYNTHETIC aperture radar , *ELECTRONIC pulse techniques , *IMAGING systems , *COHERENT radar , *ELECTRONIC systems - Abstract
Abstract: Phase noise in a radar interferogram is directly related to interferometric coherence and the look number of the interferogram. Therefore an optimal radar interferogram filter should incorporate information on both interferometric coherence and look number. We present a modification to the commonly used Goldstein filter by suggesting an improved determination of the filtering parameter. Experimental results with both simulated and real data sets show that the new filter offers much better results when used to filter radar interferograms. [Copyright &y& Elsevier]
- Published
- 2008
- Full Text
- View/download PDF
27. Contrast reversal and enhancement of phase spot array/grid with an intensity-dependent refractive index medium as phase filter
- Author
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Sendhil, Kaladevi, Vijayan, C., and Kothiyal, M.P.
- Subjects
- *
ZINC , *OPTOELECTRONIC devices , *LASERS in engineering , *INDUSTRIAL lasers - Abstract
Abstract: We report on a complete contrast reversal and contrast enhancement of a holographically recorded phase spot array/grid achieved by phase filtering, using an intensity-dependent refractive index medium, zinc tetraphenyl porphyrin as phase filter placed in the Fourier plane of the 4-f imaging system. We show that the contrast enhancement is robust and insensitive to local errors in the phase array and can be used to repair a damaged portion of the input phase array. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
- View/download PDF
28. A locally adaptive filter of interferometric phase images.
- Author
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Nan Wu, Da-Zheng Feng, and Junxia Li
- Abstract
We propose an adaptive filtering approach for interferograms, which is a modification to the Lee adaptive complex filter. Based on local frequency estimates, we compute the normal orientation of local phase fringes. A directionally dependent filtering window is aligned perpendicular to the normal orientation of local phase fringes (i.e., along local phase fringes) by interpolation, making the pixels included in the filtering window have approximately more homogeneous values. Moreover, the computation of the filter parameter does not require local phase unwrapping in the real plane. This filter minimizes the loss of signal and reduces the level of noise. By using two sets of simulated data, its effectiveness can be seen in terms of the fidelity to noise-free phases, fringe preservation, and residue reduction. [ABSTRACT FROM PUBLISHER]
- Published
- 2006
- Full Text
- View/download PDF
29. Spatial phase filtering with a porphyrin derivative as phase filter in an optical image processor
- Author
-
Sendhil, Kaladevi, Vijayan, C., and Kothiyal, M.P.
- Subjects
- *
POLARIZATION (Electricity) , *PORPHYRINS , *OPTICAL images , *ZINC - Abstract
Abstract: A robust, self-aligned, polarization independent, pure phase filter was obtained using the intensity dependent refractive index (IDRI) medium, zinc tetraphenyl porphyrin (ZnTPP), a metalloporphyrin. An optical image processor with ZnTPP as the phase filter was designed so that both weak and strong phase objects can be tested with no appreciable design modification. A good phase contrast image with negligible amplitude reduction of the input beam was achieved and the porphyrin as phase filter was found to have several specific advantages over other IDRI media. [Copyright &y& Elsevier]
- Published
- 2005
- Full Text
- View/download PDF
30. Impact of phase-filtering on optical spectral reshaping with microring resonators for directly-modulated 4-PAM signals
- Author
-
Ozolins, O., Da Ros, F., Cristofori, V., Pang, Xiaodan, Udalcovs, A., Schatz, R., Oxenløwe, L. K., Popov, Sergei, Jacobsen, G., Peucheret, C., Ozolins, O., Da Ros, F., Cristofori, V., Pang, Xiaodan, Udalcovs, A., Schatz, R., Oxenløwe, L. K., Popov, Sergei, Jacobsen, G., and Peucheret, C.
- Abstract
We investigate microring resonator (MRRs)-based optical spectral reshaping for directly-modulated 4-PAM signals. The phase-filtering of MRR, and consequent dispersion added to the signal, yields 120% reach increase compared to the 95% of amplitude-only filtering., QC 20211020
- Published
- 2018
- Full Text
- View/download PDF
31. A Phase Filtering Method with Scale Recurrent Networks for InSAR.
- Author
-
Pu, Liming, Zhang, Xiaoling, Zhou, Zenan, Shi, Jun, Wei, Shunjun, and Zhou, Yuanyuan
- Subjects
IMAGE denoising ,SYNTHETIC aperture radar ,TOPOGRAPHIC maps ,DEEP learning ,PHASE noise - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
32. An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition.
- Author
-
You, Yanan, Wang, Rui, and Zhou, Wenli
- Subjects
CITIES & towns ,SYNTHETIC apertures ,INTERFEROMETRY ,SYNTHETIC aperture radar ,RADAR interferometry ,PRINCIPAL components analysis ,BATCH processing ,MULTISENSOR data fusion - Abstract
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier's position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
33. A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage
- Author
-
Yong Wang, Dongsheng Fang, Xue Lin, Jiang Qian, and Xiaolei Lv
- Subjects
Computer science ,Noise reduction ,Science ,Denoising algorithm ,0211 other engineering and technologies ,phase filtering ,02 engineering and technology ,Regularization (mathematics) ,interferometric synthetic aperture radar (InSAR) ,nonlocal ,wavelet shrinkage ,Wavelet ,Phase noise ,Interferometric synthetic aperture radar ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,021101 geological & geomatics engineering ,business.industry ,Wavelet shrinkage ,Norm (mathematics) ,General Earth and Planetary Sciences ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Algorithm - Abstract
An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l 1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes.
- Published
- 2016
34. A modification to the Goldstein radar interferogram filter
- Author
-
Bert Kampes, P. Lilly, M.P. Stewart, I. Baran, and Zbigniew Perski
- Subjects
Synthetic aperture radar ,Computer science ,business.industry ,phase statistics ,Filter (signal processing) ,radar interferometry ,synthetic aperture radar (SAR) ,law.invention ,Interferometry ,Phase filtering ,Optics ,law ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering ,Radar ,business ,Computer Science::Formal Languages and Automata Theory ,Coherence (physics) - Abstract
We present a modification to the adaptive Goldstein radar interferogram filter which improves the quality of interferometry products. The proposed approach makes the Goldstein filter parameter alpha dependent on coherence, such that incoherent areas are filtered more than coherent areas. This modification minimizes loss of signal while still reducing the level of noise.
- Published
- 2003
- Full Text
- View/download PDF
35. High Resolution Differential Interferometric Stacking via Adaptive Spatial Phase Filtering
- Author
-
Goel, Kanika and Adam, Nico Alexander
- Subjects
Small Baseline Subset Algorithm (SBAS) ,High Resolution SAR ,Non-Urban Areas ,Phase Filtering ,Differential Interferograms - Published
- 2011
36. Superresolved femtosecond laser nanosurgery of cells
- Author
-
Pospiech, Matthias, Emons, Moritz, Kuetemeyer, Kai, Heisterkamp, Alexander, Morgner, Uwe, Pospiech, Matthias, Emons, Moritz, Kuetemeyer, Kai, Heisterkamp, Alexander, and Morgner, Uwe
- Abstract
We report on femtosecond nanosurgery of fluorescently labeled structures in cells with a spatially superresolved laser beam. The focal spot width is reduced using phase filtering applied with a programmable phase modulator. A comprehensive statistical analysis of the resulting cuts demonstrates an achievable average resolution enhancement of 30 %.
- Published
- 2011
37. Analysis of the second flight of the ANtarctic Impulsive Transient Antenna with a focus on filtering techniques
- Author
-
Dailey, Brian T.
- Subjects
- Physics, ANITA, Ultra High Energy Neutrinos, HealPix, ANITA-2 analysis, amplitude filtering, phase filtering
- Abstract
The observed cutoff in the cosmic ray spectrum leads to a highly motivated expectation of an ultra-high energy (UHE) neutrino flux, coming from interactions between the cosmic rays and cosmic microwave background photons. Although no UHE neutrinos have yet been detected; better background separation and removal will help accelerate the search.Past flights of the ANtarctic Impulsive Transient Antenna (ANITA) experiment have set the strongest limits on the UHE neutrino flux above 10^{19} eV. Due to the advanced sensitivity of future flights to both signal and anthropogenic backgrounds, the techniques used in the past analyses may not be sufficient to remove backgrounds. Here, we discuss processes developed for this analysis. First, we discuss newly techniques to filter event waveforms in both the amplitude and phase spectra. These new techniques were applied to the ANITA-2 experiment data set. We discuss a new technique developed that uses equal area bins of ice on the Antarctic continent. Further, we define a set of analysis cuts, how the analysis cuts were optimized for maximum sensitivity for UHE neutrinos, how the number of background and neutrino events were estimated. For our search, we used the maximal Kotera et. al. 2010 flux model and optimized based on this model. After optimization, we found zero events from the 10% sample passing all cuts.These techniques will prove useful for future flights of ANITA as the sensitivity of the instrument increases. The optimization procedure can also provide a starting point for future analysis. The filtering technique shown here decreased mis-reconstruction in pointing of events. The HealPix method, while requiring further refinement, shows promise by retaining valuable areas of ice that may have been removed from previous analyses.
- Published
- 2017
38. A modification to the Goldstein radar interferogram filter
- Author
-
Baran, Ireneusz, Stewart, Michael, Lilly, Peter, Baran, Ireneusz, Stewart, Michael, and Lilly, Peter
- Abstract
We present a modification to the adaptive Goldsteinradar interferogram filter which improves the quality ofinterferometry products. The proposed approach makes theGoldstein filter parameter alpha dependent on coherence, suchthat incoherent areas are filtered more than coherent areas. Thismodification minimizes loss of signal while still reducing the levelof noise.
- Published
- 2003
39. Surface Wave Identification Research in the Middle East and North Africa
- Author
-
LAWRENCE LIVERMORE NATIONAL LAB CA, Pasyanos, Michael E., Goldstein, Peter, Walter, William R., Rodgers, Arthur, LAWRENCE LIVERMORE NATIONAL LAB CA, Pasyanos, Michael E., Goldstein, Peter, Walter, William R., and Rodgers, Arthur
- Abstract
We are in the process of making improvements in surface wave identification throughout the Middle East and North Africa. Seismic surface waves have long played an important role in nuclear explosion monitoring by providing both information about the lithosphere they pass through and the source that generated them. We have made high resolution group velocity maps of the Middle East and North Africa using the tomographic inversion of more than 10,000 individual path measurements. These maps provide valuable constraints on models of the underlying structure of the region. The maps also provide estimates of expected phase of new events that can be used in phase match filters to compress the expected signal and thus improve the signal-to-noise ratio. Of very high interest is improving the performance of the traditional MS:mb discriminant and extending the discriminant down to events with smaller magnitude that are recorded at closer regional distances. This paper directly addresses the improvement that phase match filters bring to the estimation of the surface wave magnitude, MS. We have estimated MS for over 8000 events at 30 stations, both with and without the use of phase match filters, and have found a marked improvement in our ability to discriminate between earthquakes and explosions. When considering smaller magnitude events, however, several other problems emerge. In our analysis, for example, deep events can flag as "explosion-like" using the mb:MS discriminant, since they are also inefficient generators of surface waves. If unidentified as deep events or classified as ?normal? depth events, they can contaminate the actual distribution of surface wave magnitudes (and their derived discriminants) for shallow earthquakes. It is clearly very important to independently identify the source depth of these events in order to avoid misclassifying them. One possible method is through the use of waveform modeling., Proceedings of the Annual DoD/DOE Seismic Research Symposium (22nd): Planning for Verification of and Compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT) held in New Orleans, Louisiana on 13-15 September 2000. U.S. Government or Federal Rights. The original document contains color images.
- Published
- 2000
40. Superresolved femtosecond laser nanosurgery of cells
- Author
-
Moritz Emons, Matthias Pospiech, Uwe Morgner, Kai Kuetemeyer, and Alexander Heisterkamp
- Subjects
Materials science ,Microscope ,Dewey Decimal Classification::500 | Naturwissenschaften::570 | Biowissenschaften, Biologie ,Laser scanning ,Optical sectioning ,Exit pupil ,Femtoseconds ,ocis:(170.0180) Microscopy ,Super-resolved lasers ,Two-photon absorption ,law.invention ,Resolution enhancement ,Optics ,Phase filtering ,law ,ddc:570 ,Microscopy ,Optical constants ,Focal spot ,ddc:610 ,ocis:(140.7090) Ultrafast lasers ,business.industry ,Laser ,Atomic and Molecular Physics, and Optics ,ocis:(100.6640) Superresolution ,ocis:(070.6120) Spatial light modulators ,Phase modulator ,Phase modulation ,ocis:(350.5730) Resolution ,Nanosurgery ,Femtosecond ,ocis:(180.2520) Fluorescence microscopy ,Dewey Decimal Classification::600 | Technik::610 | Medizin, Gesundheit ,business ,Biotechnology - Abstract
Multiphoton fluorescence microscopy based on femtosecond laser scanning is a powerful technique for three dimensional optical sectioning in life sciences. The method is based on the simultaneous absorption of two or three photons in the focal volume of a high NA microscope objective. The same setup is suited for nanodissection of living cells and subcellular structures. A very small lateral extent of the modified focal volume is required to minimize collateral damage in the vicinity of the laser focus and to improve long-term cell viability. By using high NA microscope objectives and laser pulse energies close to the ablation threshold, the lateral extent of the modified material is limited to less than 1 µm. This diffraction limited resolution can be further improved by techniques generally referred to as superresolution. These are achieved by controlling the phase of the laser beam with a diffractive filter placed at the exit pupil of an optical system. We integrated this technique into nanosurgery of cells, which we demonstrate here for the first time.
41. Spatial phase filtering based on the intensity-dependent refractive index of PbS nanocomposite film
- Author
-
Pushpa Ann Kurian and C. Vijayan
- Subjects
Nonlinear optics ,Refractive index ,Physics::Optics ,X-ray optics ,Nonlinear refractive index coefficient ,Nanocomposite materials ,Imaging techniques ,Optoelectronic devices ,Helium ,Industrial and Manufacturing Engineering ,law.invention ,Nanocomposites ,Z-scan technique ,Phase filtering ,law ,Self-adjusting ,Phase (matter) ,Refractometers ,Phase-contrast imaging ,A-thermal ,Thermo-optical ,Non-Linearity ,Stable films ,Laser excitation ,Contrast media ,Nanocomposite film ,Refraction ,Self-adaptive ,Continuous wave ,Light refraction ,Optical nonlinearity ,Continuous Wave ,Materials science ,Materials Science (miscellaneous) ,In-phase ,Neon ,Non-linear optical ,PbS nanocrystal ,Optics ,Transparent objects ,Business and International Management ,Nonlocal ,Nanocomposite ,business.industry ,Laser ,Intensity-dependent ,He-Ne lasers ,4f imaging systems ,business ,Excitation sources ,All-optical - Abstract
We demonstrate the use of stable films containing PbS nanocrystals as media for self-adaptive phase filtering in phase contrast imaging of transparent objects by a cost-effective exploitation of nonlinear optical refraction in a simple, all-optical, and self-adjusting 4f imaging system. The optical nonlinearity is characterized by z-scan technique using a continuous wave He-Ne laser as the excitation source. The mechanism of nonlinearity in this case is mainly due to the nonlocal thermo-optical interaction between the laser beam and the sample. The value of nonlinear refractive index coefficient is found to be -3:5 � 10-7 cm2=W. The nanocomposite material shows a thermal lens effect and is a potential candidate for phase contrast imaging. � 2009 Optical Society of America.
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