非下采样Contourlet变换耦合锐度制约的遥感图像融合

当前多数遥感图像融合算法主要是依靠比值法选取全色图像或多光谱图像中的其中一个高频子带作为高频融合系数,忽略了另一个高频系数所包含的信息,易导致融合图像出现模糊以及光谱失真等不足.对此,本文提出了基于非下采样Contourlet变换与锐度制约模型的遥感图像融合算法.通过亮度-色调-饱和度(IHS)变换,获取多光谱图像的I,H,S分量,利用非下采样Contourlet变换对多光谱图像的I分量以及全色图像进行多尺度精细分解,得到相应的低频子带与高频子带;利用像素点邻域的像素值之差构造锐度制约模型,完成低频子带的融合.考虑多光谱图像中I分量与全色图像的高频子带特征,构造高频子带融合模型,完成高频子带的融合;将融合后的高频子带与低频子带通过非下采样Contourlet逆变换,输出融合图像的亮度分量珔I,将珔I与H,S分量进行IHS逆变换,形成最终的融合图像.仿真实验显示,与当前遥感图像融合方法相比,所提方法的融合图像具有更高的视觉质量,可保留更多的光谱以及边缘等图像细节信息.

Remote Sensing Image Fusion Based on Nonsubsampled Contourlet Transform and Sharpness Constraint

Most of the current fusion algorithms of remote sensing image fusion mainly rely on the ratio method to select the high frequency subbands of the panchromatic or multispectral images as high frequency fusion coefficients. Because the method ignores the information contained in the other high frequency coefficients, it is easy to lead to the inadequacy of the fusion image and the spectral distortion. For this reason, a remote sensing image fusion algorithm based on the coupled nonsubsampled contourlet transform and the acuity constraint model has been proposed in this paper. The I, H, and S components of the multispectral image are obtained by the brightness to hue saturation (IHS) transformation. The I component and the panchromatic image of the multispectral image are decomposed by non subsampled contourlet transform to obtain the low frequency subband and the high frequency subband of the image. The sharpness constraint model is constructed by the difference between pixel values in the neighborhood of pixels, so as to complete the fusion of low frequency sub-band. Based on the high frequency subband features of I components in multispectral images, the high-frequency subband fusion model is constructed by combining the high frequency subband features of the panchromatic image to complete the fusion of high frequency subbands. The fusion high frequency subband and low frequency subband are replaced by the non subsampled contourlet inverter to obtain the brightness component I of the fused image, and the T and H and S components are inverting IHS to obtain the fused image. The simulation experiment shows that compared with the current remote sensing image fusion method, the fusion image of the proposed method can retain more spectral and edge and other image details, so that the remote sensing image has a better fusion effect.