论文部分内容阅读
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。
In this paper, an image fusion method based on nonsubsampled Contourlet transform (NSCT) and pulse coupled neural network (PCNN) is proposed to solve the problem of multi-polarization SAR image fusion. This method decomposes the registered multipolarized SAR images by NSCT to obtain the low frequency subband coefficients and the band pass subband coefficients. The simplified PCNN model is used to make intelligent decisions on the low frequency subband and high frequency subband coefficients respectively, And NSCT inverse transform to obtain a fusion image. Experiments show that this method can preserve the information of the original polarimetric SAR image to the maximum extent, and the fusion effect is better than the fusion method based on single pixel and local feature.