基于NSST变换与自适应PCNN的多特征遥感图像融合

摘要

As the remote sensing image fusion algorithm based on non－subsampled contourlet transform(NSCT)has high calculation complexity and can not extract details from source images effectively,a new multi －feature remote sensing image fusion algorithm based on NSST transform and adaptive PCNN is proposed. Firstly,intensity component V of multi－spectral image is extracted by HSV transform,and the intensity component V and PAN image are decom-posed by NSST. Secondly,for the low frequency sub－band,an adaptive PCNN fusion rule is presented,regional aver-age gradient is utilized as the linking intensity,and a modified spatial frequency is adopted as the input to motivate PCNN. For the high frequency sub－band,a fusion rule based on the multi －feature is employed. Finally,the fused images are obtained by inverse NSST transform and inverse HSV transform. The experimental results show that com-pared with classical remote sensing image fusion algorithms,the proposed fusion algorithm can improve the quality of the fused image,and has the better performance in visual effect and objective evaluation metrics.%针对基于NSCT变换的遥感图像融合算法存在计算复杂度高,细节表现能力不足的问题,本文提出了一种基于NSST变换与自适应PCNN的多特征遥感图像融合算法.首先,利用HSV变换提取MS图像的亮度分量V,并将得到的亮度分量V与PAN图像分别进行NSST变换;其次,对于低频子带,提出了一种基于自适应的PCNN融合规则,将空间频率和区域平均梯度分别作为PCNN的外部激励和链接强度;对于高频子带,采用基于多特征的融合规则;最后,进行逆NSST变换和逆HSV变换得到融合图像.仿真实验表明,该算法与一些经典的融合算法相比不仅可以提高图像融合质量,在视觉效果和客观指标上也都有良好的表现.