This paper focuses on the study of a multi-frequency interferometric coherence characteristics analysis of typical objects for coherent change detection. Coherent change detection utilizes the phase difference between two or more SAR images to detect potential changes in the scene. It makes a difference in civilian and military applications. However, the relationship between the coherence of typical objects and SAR frequency has not been fully studied, which restricts the quality of the detection results. To address this problem, this paper conducts research on the relationship between the coherence of typical objects and SAR frequency, and the coherence characteristics are obtained through statistical analysis. In order to illustrate the relationship more clearly, the actual experimental data obtained by the DVD-InSAR system developed by the Aerospace Information Research Institute, Chinese Academy of Sciences, are utilized. The experimental results show that the coherence characteristics of typical objects are different, and this finding can provide strong support for developing change-detection applications.
With increasing demands from both military and civilian applications, ground moving-target imaging is becoming one of the important research topics for high-resolution SAR systems. However, the existing moving-target imaging methods are not suitable for high-resolution SAR because of their low parameter estimation accuracy and high computational complexity. To solve the problem, an improved ground moving-target parameter estimation and imaging method is proposed. First, the third-order phase model of the uniformly accelerated target signal is constructed, and the Hough transform and the second-order Keystone transform (SOKT) are used to correct the range cell migration into one range cell to achieve target coherent accumulation. Secondly, a delayed cross-correlation function (DCCF) is constructed to reduce the order of the range migration phase response in the slow time domain, and the coupling degree between the cross-correlation peak position and the range migration is reduced, so that the obtained DCCF has a higher gain, which ensures the accuracy of parameter estimation. Parameter estimation is simplified to peak detection by the Shift-And-Correlation (SAC) algorithm and two-dimensional Fourier transform (2D-FFT), avoiding parameter search. Compared with the existing methods, the proposed method has better focusing effect and lower computational complexity. Finally, simulation and measured data are given to verify the effectiveness of the proposed method.
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