The adaptive phase optimization algorithm for DSInSAR driven by priori information
Phase optimization is a key step in distributed scatterers interferometric synthetic ap-erture radar(DSInSAR)technique to improve the phase signal-to-noise ratio.In order to im-prove the severe loss of phase information at dense fringes corresponding to large gradient de-formation in mining areas using current phase optimization algorithms,an adaptive phase opti-mization algorithm for DSInSAR driven by a priori information is proposed.The proposed al-gorithm first uses the conventional small baseline subset InSAR technique to invert the initial time series deformation phase,and obtains a priori deformation phase information through the preprocessing for the time series deformation phase.Then,the priori deformation phase is re-moved from the original single look complex(SLC)phase to obtain the SLC residual phase,and the residual phase optimization model is constructed by combining the coherence-power-weighting strategy.Furthermore,the final optimized phase is estimated by compensating for a priori deformation phase.The experimental results show that the proposed algorithm can effec-tively take into account the phase information protection and noise suppression in the dense fringe region corresponding to the large gradient deformation field,and effectively improve the measurement point density and monitoring accuracy in the large gradient deformation region.In summary,the proposed algorithm has better adaptive effect and optimization performance than the conventional optimization algorithm.
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