基于PDE和正则化的多极化SAR图像特征增强方法

Xintong Tan, Jubo Zhu
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引用次数: 0

摘要

本文主要研究多极合成孔径雷达(SAR)图像的特征增强问题。提出了一种基于偏微分方程和正则化的新方法,是对原偏微分方程和正则化方法的扩展。它包含了抑制散斑的PDE项和增强强散斑的稀疏性约束项。将探测到的ROA算子与多极化SAR图像的幅值相结合,建立了PDE项。稀疏性约束项包含图像的结构信息和稀疏性。实验结果表明,该方法能有效地抑制SAR图像中的散斑噪声,增强SAR图像的特征,特别是结构和边缘特征。
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Feature enhancement for multi-polarimetric SAR images: A novel approach based on PDE and regularization
This paper aims at the feature enhancement for multi-polarimetric synthetic aperture radar (SAR) images. A novel approach based on PDE and regularization which is an extension of the original PDE and regularization methods is proposed. It contains the PDE term for speckle suppression and the sparsity constraint term for strong scatter enhancement. The PDE term is established by combining the ROA detected operator and the amplitude of the multi-polarimetric SAR images. The sparsity constraint term contains the structural information and the sparsity of the images. Experiments on the measured multi-polarimetric SAR images show that the proposed approach can efficiently suppress speckle noise and enhance features especially structural and edge features in SAR images.
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