An Augmented Lagrangian Method for Complex-Valued Compressed SAR Imaging
An Augmented Lagrangian Method for Complex-Valued Compressed SAR Imaging
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DOI：
10.1109/tci.2016.2580498
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发表时间：
2016-09-01
影响因子：
5.4
通讯作者：
Cetin, Mujdat
中科院分区：
文献类型：
--
作者：
Guven, H. Emre;Gungor, Alper;Cetin, Mujdat
在本文中，我们提出了一个约束优化公式中复杂合成孔径雷达（SAR）成像问题的解决方案，其中目标函数包括复值反射率场的l(1)范数和总变化幅度的组合。我们提出的技术依赖于优化问题解决的最新进展，基于增广拉格朗日方法，特别是乘法器的交替方向方法（ADMM）。对于复值SAR图像，我们严格推导出与反射率矢量和幅度总变化成本函数的幅度线性变换相关的近端映射算子，从而能够使用ADMM技术获得雷达成像的计算效率解决方案。我们研究了基于1范数和幅度总变化的加权和的多特征（稀疏和分段恒定）的技术。对于允许正演模型为酉变换的问题，我们推导出一种算法的快速实现，每次迭代只使用两个变换。在TerraSAR-X和sarper机载SAR系统的真实数据上的实验结果证明了该方法的有效性。
In this paper, we present a solution to the complex synthetic aperture radar (SAR) imaging problem within a constrained optimization formulation where the objective function includes a combination of the l(1)-norm and the total variation of the magnitude of the complex valued reflectivity field. The technique we present relies on recent advances in the solution of optimization problems, based on Augmented Lagrangian Methods, and in particular on the Alternating Direction Method of Multipliers (ADMM). We rigorously derive the proximal mapping operators, associated with a linear transform of the magnitude of the reflectivity vector and magnitude-total-variation cost functions, for complex-valued SAR images, and thus enable the use of ADMM techniques to obtain computationally efficient solutions for radar imaging. We study the proposed techniques with multiple features (sparse and piecewise-constant in magnitude) based on a weighted sum of the 1-norm and magnitude-total-variation. We derive a fast implementation of the algorithm using only two transforms per iteration for problems admitting unitary transforms as forward models. Experimental results on real data from TerraSAR-X and SARPER-airborne SAR system developed by ASELSAN-demonstrate the effectiveness of the proposed approach.