非下采样Contourlet变换与脉冲耦合神经网络相结合的SAR与多光谱图像融合

由于获取地物波谱信息的波段范围及成像方式的不同,SAR与多光谱图像所得到的信息有很大差异,而且SAR图像会受到严重的相干斑噪声干扰,因此SAR与多光谱图像的融合很难获得满意的效果。考虑到非下采样Contourlet变换(NSCT)相比于其他多尺度几何分析方法的优势,提出了一种NSCT与脉冲耦合神经网络(PCNN)相结合的SAR与多光谱图像融合方法。源图像首先经过NSCT分解获得不同尺度多个方向下的分解系数,将分解系数的高斯拉普拉斯算子能量作为脉冲耦合神经网络模型的输入,具有较大点火频率的系数将被选择作为融合图像的系数,最后经过NSCT重构得到最终的融合图像。实验结果表明,这种算法无论在主观视觉还是在客观指标上都要优于之前的许多算法。

SAR and multispectral image fusion algorithm based on pulse coupled neural networks and non-subsampled Contourlet transform

SAR and optical images have large differences in imaging-mechanism and spectral characteristics. Moreover, SAR images are always severe contaminated by speckle noise. Consequently, it is very difficult to obtain satisfying results while fusing SAR and optical images. Considering the advantage of non-sampled Contourlet transform(NSCT) comparing with other multiscale decomposition methods, a method of image fusion based on pulse coupled neural networks(PCNN) and NSCT is proposed. The source images are first decomposed in the NSCT domain. Energy of log in the NSCT domain is the input to motivate PCNN and coefficients in NSCT domain with high firing frequency are selected as coefficients of the fused image. Then the final fused image is obtained by NSCT reconstruction. Experimental results demonstrate that the proposed algorithm outperforms many other algorithms in both objective criteria and visual appearance.