4 October 2017 Regularization parameter estimation for point-based synthetic aperture radar image feature enhancement based on Mellin transform
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Abstract
Considering the sparseness of scatterers in the scene of a synthetic aperture radar (SAR) image, we propose a modified model for SAR images with enhanced features by automatically choosing variable lk-norm and regularization parameter. The approach is based on a regularized reconstruction of the scattering field, which employs prior information of the region of interest. It leads to an alternating iterative algorithm for the modeling. The method is constructed based on variable lk-norm and regularization parameter. Here, k is a function of the imaged region and it could be estimated during the iteration process to the scattering field. The regularization parameter is changing because it is being determined by k. Moreover, the parameter estimators of the presented model are derived by applying the method of log cumulants-based on Mellin transform. Compared to conventional SAR regularization methods, the proposed method reconstructs images with increased resolution, reduced clutter, and reduced computation cost. We demonstrate the performance of the method on real SAR scenes. The experiment results of measured SAR data prove the effectiveness.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shujuan Peng, Shujuan Peng, Changwen Qu, Changwen Qu, Jianwei Li, Jianwei Li, Zhi Li, Zhi Li, Bing Deng, Bing Deng, } "Regularization parameter estimation for point-based synthetic aperture radar image feature enhancement based on Mellin transform," Journal of Applied Remote Sensing 11(4), 045002 (4 October 2017). https://doi.org/10.1117/1.JRS.11.045002 . Submission: Received: 9 March 2017; Accepted: 7 September 2017
Received: 9 March 2017; Accepted: 7 September 2017; Published: 4 October 2017
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