Article

Estimation Method for InSAR Interferometric Phase Based on Generalized Correlation Steering Vector

IEEE Transactions on Aerospace and Electronic Systems (Impact Factor: 1.76). 08/2010; 46(3):1389 - 1403. DOI: 10.1109/TAES.2010.5545196
Source: IEEE Xplore

ABSTRACT

We propose a new method, based on the generalized correlation steering vector, to estimate synthetic aperture radar interferometry (InSAR) interferometric phase. In this method the generalized correlation steering vector with a large coregistration error is determined according to the joint data vector. The generalized correlation steering vector is then used to estimate the InSAR interferometric phase. The method can simultaneously auto-coregister the synthetic aperture radar (SAR) images and reduce the interferometric phase noise. Theoretical analysis and computer simulation results show that the method can provide an accurate estimate of the terrain interferometric phase (interferogram), even if the coregistration error reaches one pixel.

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Available from: Guisheng Liao
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    • "The problem here is that when the quality of an interferogram is very poor due to a large coregistration error, it is very difficult for those methods to retrieve the true terrain interferometric phases. In fact, the interferometric phases we obtained are random signals with their variances being inversely proportional to the correlation coefficients between the corresponding pixel pairs of the two coregistered SAR images [2] . Therefore, the terrain interferometric phases should be estimated using a statistical method. "
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    ABSTRACT: In this paper we propose a method to estimate the InSAR interferometric phase using the correlation weight subspace projection technique. In the method the correlation weight data vector is constructed, thus the noise subspace dimension of the corresponding covariance matrix will not be affected by the coregistration error, then avoiding the trouble of calculating the noise subspace dimension before estimating the InSAR interferometric phase. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. The effectiveness of the method is verified via simulated data.
    Full-text · Article · Oct 2011
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    • "M is the terrain interferometric phase to be estimated, : denotes the Hadamard product, x (i) is the complex magnitude vector (i.e., complex reflectivity vector of scene received by the satellites) of pixel i, and n (i) is the additive noise term. The complex data vector s (i) can be modeled as a joint complex circular Gaussian random vector[1][2] with zeromean and the corresponding covariance matrix C s (i) is given "
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    ABSTRACT: In this paper, an improved joint subspace projection method for synthetic aperture radar interferometry (InSAR) interferogram filtering is proposed. Benefiting from the new formulation of joint data vector, the method does not need to calculate the noise subspace dimension before estimating the InSAR interferometric phase, thus avoiding the effect on the estimation of the InSAR interferometric phase due to the inaccuracy of the noise subspace dimension. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously.
    Full-text · Conference Paper · Oct 2010
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    • "< < pixel and its direction is right(i.e., the pixel of the image from the second satellite is shifted to the right compared to the pixel in the first satellite image), the formulation of the joint data vector ( ) i js is shown in Fig. 1, where circles represents SAR image pixels and i denotes the desired pixel pair whose interferometric phase is to be estimated. Here, ( ) i js is called the optimal joint data vector[18] [19] [20], and the approach to determine the data vector is presented in literature[18] [19] "
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    ABSTRACT: In this paper, we propose a robust estimation method to coregistration error for synthetic aperture radar interferometry (InSAR) interferometric phase. In the method, the optimal joint data vector is determined, the true steering vector is computed according to the data vector, and then the beamforming technique with the steering vector is used to estimate the InSAR interferometric phase. The method can carry out image coregistration and interferometric phase estimation simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) even when the coregistration error reaches one pixel.
    Full-text · Conference Paper · Jan 2010
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