Super-resolution SAR imaging via nonlinear regressive model parameter estimation method

A novel SAR super-resolution imaging method is described Firstly, SAR image peak extraction is carried out in the image domain and the coarse feature parameter estimation is obtained. Secondly, Parameter estimation of nonlinear regressive model is carried out in the phase history domain and the fine...

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Main Authors: Wang Xiong-liang, Wang Zheng-ming
Format: Conference Proceeding
Language:English
Subjects:
Frequency
High-resolution imaging
History
Image resolution
Light scattering
Parameter estimation
Phase estimation
Radar polarimetry
Radar scattering
Scattering parameters
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Wang Zheng-ming
description A novel SAR super-resolution imaging method is described Firstly, SAR image peak extraction is carried out in the image domain and the coarse feature parameter estimation is obtained. Secondly, Parameter estimation of nonlinear regressive model is carried out in the phase history domain and the fine feature parameter estimation is obtained. Finally, from the estimated parameter and based on the point-scattering model, the simulated phase history data of large dimensions is generated. By FFT imaging, higher resolution image is obtained. Experimental examples have shown that this method offer significant advantages over the FFT methods to better resolve the dominant target scatterers.
doi_str_mv 10.1109/CGIV.2005.72
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Frequency
High-resolution imaging
History
Image resolution
Light scattering
Parameter estimation
Phase estimation
Radar polarimetry
Radar scattering
Scattering parameters
title Super-resolution SAR imaging via nonlinear regressive model parameter estimation method
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