A Joint Image Coregistration, Phase Noise Suppression, and Phase Unwrapping Method Based on Subspace Projection for Multibaseline InSAR Systems

Li, Zhenfang; Bao, Zheng; Suo, Zhiyong

doi:10.1109/tgrs.2006.888143

Cited by 32 publications

(27 citation statements)

References 29 publications

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“…1 as an example, we assume that there is no coregistration error in the azimuth direction, the number of the neighbouring pixels used to construct the joint complex interferometric phase data vector is 3 and the processing pixel in the interferograms is marked as (i, j). The joint complex D-InSAR phase data vector jp(i, j) similar to the data vector constructed in [11,16,17], can be formulated as…”

Section: Mathematical Modelmentioning

confidence: 99%

Robust differential interferometric synthetic aperture radar deformation phase estimation method based on covariance matrix information of multiple pixels

Suo

Bao

2014

IET Radar, Sonar & Navigation

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Differential interferometric synthetic aperture radar (D-InSAR) is a powerful technique to detect ground deformation, and the deformation estimation accuracy is directly affected by the D-InSAR phase accuracy. Coregistration of synthetic aperture radar (SAR) images and/or interferograms is one of the key steps in the D-InSAR flowchart, and the coregistration errors inevitable between SAR images will greatly decrease the D-InSAR phase estimation accuracy. In this study, the authors propose a robust approach to estimate the deformation phase by using the central pixel and its neighbouring pixels simultaneously, which is coupled with the signal subspace fitting or the covariance matrix fitting. The method can accomplish image auto-coregistration and suppress the D-InSAR phase noise together. Furthermore, compared with the required coregistration accuracy (no less than 1/10 pixel) for the conventional D-InSAR processing, the required image and/or interferogram coregistration error of the proposed method can reach 1 pixel, which greatly releases the district limitation on coregistration in D-InSAR processing. The performance is investigated by using simulated data and real ENVISAT ASAR data.

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Section: Mathematical Modelmentioning

confidence: 99%

Robust differential interferometric synthetic aperture radar deformation phase estimation method based on covariance matrix information of multiple pixels

Suo

Bao

2014

IET Radar, Sonar & Navigation

Self Cite

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“…Let us compare the performance of the proposed method with the method in [25]. It is well known that the dominant computational complexity of an algorithm is determined by that of the eigendecomposition or inversion of the covariance matrix, and both these computational cost are equal to O(M 3 ), where O(·) denotes "order of".…”

Section: Performance Investigationmentioning

confidence: 99%

“…When we select a 3 × 3 window to construct the multibaseline joint block vector, the dimensions of the covariance matrix using the method in this paper are M × M . And dimensions of that in [25] are 9M × 9M . That is, the dimensions of the covariance matrix in [25] are 9 times that of the proposed method.…”

Section: Performance Investigationmentioning

confidence: 99%

“…And dimensions of that in [25] are 9M × 9M . That is, the dimensions of the covariance matrix in [25] are 9 times that of the proposed method. Furthermore, the dimensions of the covariance matrix of the proposed method only relate to the number of the array phase centers and are uncorrelated with how to choose the pixel window sizes to construct the multibaseline joint block vector.…”

Section: Performance Investigationmentioning

confidence: 99%

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Application of Array Processing Techniques in Multibaseline Insar for High-Resolution Dem Reconstruction

Mao¹,

Liao²

2008

PIER M

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Abstract-This work is concerned with generating the digital elevation models (DEMs) from the SAR images of the region of interest using multiple sensor arrays that are fixed on the distributed satellites. We present an exact estimate of the unwrapped phase and the relationship between the unwrapped phase and the terrain height. The optimum scheme that jointly processes the signals from all sensors is based on the model of the multibaseline joint block vector. The method can simultaneously coregister the SAR images, phase unwrapping and DEM generation in the presence of the large coregistration errors. The performance of our approach is verified by a series of simulation experiments based on the distributed sensor arrays.

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“…However, the existing phase unwrapping algorithms face three major challenges in the areas of consistency, accuracy, and computing speed. Therefore some scholars have studied phase filtering and unwrapping joint processing algorithm [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A Method of Filtering and Unwrapping Sar Interferometric Phase Based on Nonlinear Phase Model

Huang¹,

Wang²

2014

PIER

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Abstract-This paper presents a new efficient algorithm of filtering and unwrapping phase images for interferometric synthetic aperture radar (InSAR) based on nonlinear phase model. First, we analyzed the statistical and signal properties of interferometric phase, and proposed the concept of nonlinear phase model. The model of reflecting topographic contour is used to approximate the interferometric phase variation occuring over the local window. And the lower amplitude bound of the principal vector is decided and the value solution method is given, which can solve effectively and adaptively the nonlinear factor of the phase. Second, we studied the application of nonlinear phase model in interferometric phase filtering. When compared with other advanced filters, the nonlinear phase compensation filter, with a higher computation efficiency and a better phase estimation accuracy in low coherence areas, has a stronger ability to reduce interferometric phase noise in rugged terrain. Finally, we introduced the nonlinear phase model to phase unwrapping, which increased the reliability of integration path and the accuracy of the phase gradient, and improved effectively the performance of phase unwrapping. And the real data processing results demonstrated the validity of the proposed nonlinear phase model and of the corresponding solutions.

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A Joint Image Coregistration, Phase Noise Suppression, and Phase Unwrapping Method Based on Subspace Projection for Multibaseline InSAR Systems

Cited by 32 publications

References 29 publications

Robust differential interferometric synthetic aperture radar deformation phase estimation method based on covariance matrix information of multiple pixels

Robust differential interferometric synthetic aperture radar deformation phase estimation method based on covariance matrix information of multiple pixels

Application of Array Processing Techniques in Multibaseline Insar for High-Resolution Dem Reconstruction

A Method of Filtering and Unwrapping Sar Interferometric Phase Based on Nonlinear Phase Model

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