A Fast Phase Optimization Approach of Distributed Scatterer for Multitemporal SAR Data Based on Gauss-Seidel Method

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS(2022)

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摘要
Distributed scatterer (DS) interferometric synthetic aperture can retrieve maximum available information by jointly processing persistent scatterers (PSs) and DSs. Unlike PSs, DSs are vulnerable to temporal, geometrical, and volumetric decorrelation. The phase optimization of DSs is essential for reliable parameter estimation. However, the preprocessing of DSs is very computationally expensive, and this drawback limits its engineering application to some degree. To improve computational efficiency, a fast scheme for reliable phase optimization of DSs is proposed based on the coherence-weighted model in this letter. The Gauss-Seidel iteration, having the advantages of fast convergence rate and small data memory, is introduced to solve the adopted phase optimization model. Experiments both on simulated data and real data are used to verify the reliability and efficiency of the presented method in this letter.
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关键词
Optimization, Coherence, Jacobian matrices, Image reconstruction, Reliability, Principal component analysis, Radar polarimetry, Coherence-weighted model, computational efficiency, distributed scatterer (DS), Gauss-Seidel iteration, persistent scatterer (PS)
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