采用自适应字典学习的InSAR降噪方法

doi:10.3969/j.issn.1001-2400.2016.01.004

Abstract

Abstract:

We consider the phase noise filtering problem for interferometric synthetic aperture radar (InSAR) based on the dictionary learning technique. Due to the non-convexity of the optimization problem is difficult to solve. By using the splitting technique and employing the augmented Lagrangian framework, we obtain a relaxed nonlinear constraint optimization problem with l₁-norm regularization which can be solved efficiently by the alternating direction method of multipliers (ADMM). Specifically, we firstly train dictionaries from the InSAR complex phase data, and then reconstruct the desired complex phase image from the sparse representation. Simulation results based on simulated and measured data show that this new InSAR phase noise reduction method has a much better performance than several classical phase filtering methods in terms of residual count, mean square error (MSE) and preservation of the fringe completeness.

Key words: interferometric synthetic aperture radar, phase noise reduction, dictionary learning, l<sub>1</sub>-norm regularization, alternating directional method of multipliers

LUO Xiaomei;SUO Zhiyong;LIU Qiegen. InSAR noise reduction using adaptive dictionary learning[J].J4, 2016, 43(1): 18-23.

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InSAR noise reduction using adaptive dictionary learning

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