Curvelet变换是继小波变换之后，能更适合于图像处理的一种新的多尺度变换分析方法，它比小波变换更加适合分析2维图像中的曲线或直线状的边缘特征，同时也具有很强的方向性。为了将该变换应用于图像融合，首先对第2代Curvelet变换理论进行了综述，然后在对基于第2代Curvelet变换的遥感图像融合方法进行研究的基础上，提出了一种与IHS变换结合的融合方法。最后用高分辨率全色图像与低分辨率多谱图像进行了融合实验，实验结果表明，将Curvelet变换引入图像融合，能够更好地提取原始遥感图像的特征，不仅可为融合图像提供更多的信息，而且融合图像能在较好地保留光谱信息的同时，使空间细节信息也得到增强。

Curvelet, as a new multi scale analysis algorithm, is more suitable for image processing than wavelet,and is more appropriate for the analysis of the image edges such as curve and line characteristics. In addition, the representation contains more directional information. In this paper the theory and implementation of the second generation Curvelet transform is reviewed. The methods of remote sensing image fusion are proposed based on Curvelet transform and the scheme using Curvelet and IHS transform is described in detail. The processing of fusion is performed with the high resolution panchromatic and low resolution multispectral remote sensing images. The result of experiment indicates that the performance of Curvelet based image fusion method excels those of based IHS or wavelet transform. When the Curvelet transform is introduced to image fusion,original images are characterized better and more information for fusion is obtained.It not only preserves spectral information of the original multispectral image well,but also enhances spatial information with more datails.