中文摘要：

本文针对多极化SAR图像的融合问题，提出了一种基于非下采样Contourlet变换（NSCT）与脉冲耦合神经网络（PCNN）的图像融合方法。此方法用NSCT对已配准的多极化SAR图像进行分解，得到低频子带系数和各带通子带系数；采用简化的PCNN模型分别对低频子带和高频子带系数进行智能决策，并进行NSCT逆变换得到融合图像。经实验表明该方法能够最大程度地保留原始极化SAR图像的信息，融合效果好于基于单个像素和局部特征的融合方法。

英文摘要：

Considering the limitations of the traditional fusion rules based on pixel or local characteristics, Pulse Coupled Neural Network （PCNN） with the global coupled property was introduced to image fusion. Combining with the excellent characteristics including multi-scale, multi-direction and shift-invariant in the Nonsubsampled Contourlet Transform（NSCT）, a new fusion scheme based on NSCT and PCNN was proposed to fuse multi-polarization Synthetic Aperture Radar （SAR） images in the paper. The simplified PCNN model was used to make decision on the sub-band coefficients selection in NSCT domain. Final ly, the method was examined by using ALOS dual-polarization SAR images and compared with some regu lar fusion algorithms based on multi-scale decomposition. Experimental results indicated that the proposed method would be more effective to fuse the multi-polarization SAR images than the pixel-based algorithm and the windows-based algorithm.