基于平稳Contourlet变换的极化SAR图像融合

提出一种新颖而有效的基于平稳Contourlet变换的极化SAR图像融合算法。平稳Contourlet变换是一种具有几何信息的灵活多尺度、多方向和平移不变性的图像分解变换，与小波变换相比，对图像分析很重要的沿曲面任意方向反映的细节更容易调整。采用平稳Contourlet变换对多个单极化强度图像进行分解，对于低频系数和方向高频系数采用最优加权算法实现极化图像的融合处理。实验结果表明，该算法与PWF算法相比在保留原始图像边缘和纹理信息同时，可以有效地抑制相干斑噪声的影响，取得较好的融合视觉效果。

Fusion of Polarimetric SAR Image Based on Stationary Contourlet Transform

A novel and efficient fusion method for polarimetric SAR image based on stationary contourlet transform is proposed. Several single-polarimetric-channel SAR intensity images are decomposed using stationary contourlet transform. Low-pass coefficients and the directional high-frequency coefficients are selected by optimal weighted sum of intensities algorithm for fusion. Experimental results show that compared with PWF de-speckling algorithm, the proposed algorithm can get better visual effect and preserves image details, and the significant information of original image like textures and contour details is well maintained.