多极化SAR图像联合稀疏去噪

Multipolarimetric SAR Image Denoising Based on Joint Sparse Representation

给出了一种基于联合稀疏表示的多极化合成孔径雷达(SAR)图像滤波算法。首先,利用三个极化通道(HH、HV、VV)的部分SAR图像数据进行字典联合训练;然后,对极化SAR的三个通道图像构建联合稀疏描述模型;最后,采用正交匹配追踪算法求解联合稀疏系数,重构每个通道的图像。文中采用美国AIRSAR实测半月湾数据进行实验,并与每个通道图像单独稀疏去噪再合成的功率图像结果进行比较,结果表明:该算法不仅对图像的斑点噪声抑制效果明显,而且边缘特性和强散射点目标幅值特征保持效果良好。

A novel speckle reduction algorithm based on joint sparse representation of muhipolarization synthetic aperture radar （SAR） image is presented. First, the data of SAR image are used to train the dictionary. Then joint sparse model is constructed. Finally, joint sparse coefficient is solved with orthogonal matching pursuit algorithms, to reconstruct the clean images of each chan- nel . Experimental results with the data of hayward area from AIRSAR system show that the proposed method is effective both on speckle reduction and strong scattering point target signature preservation compared with the synthetic power image results of each channel image sparse denoising separately.