基于非下采样剪切波变换和导向滤波的遥感图像融合方法

Pan-Sharpening with Non-Subsample Shearlet Transform and Guided Filtering

提出一种新的基于非下采样剪切波变换和导向滤波的遥感图像融合方法,该方法首先对多光谱图像和全色图像使用非下采样剪切波分解,对分解后的高频图像采用绝对值最大法进行融合,对低频图像采用基于导向滤波的方法进行融合。该方法有效利用了导向滤波的边缘保持性,且减少了传统多尺度分解造成的伪吉布斯现象。通过GeoEye卫星数据进行仿真实验,结果表明:该方法使得融合后的图像能既能保持较好地光谱信息,又提升了图像的空间结构信息。

In this paper,in order to reduce the color distortion and enhance the spatial information of fused images,we proposed a novel pan-sharpening method based on non-subsample shearlet transform and guided filtering.First,a geometric multiscale analysis tool,non-subsampled shearlet transform,was used to capture directional details of multispectral and panchromatic images at multiple scales.Then,the high-frequency images were fused via the abs-max rules.Guided filtering can be applied to the low-frequency image.Some simulation experiments are to be taken on GeoEye satellite,and the experimental results show that our proposed method can reduce distortions in both the spectral and spatial domains,and outperform some related methods in terms of both visual results and numerical guidelines.