曲波变换是一种更适合于图像处理的多尺度几何分析方法, 具有比小波变换更强的方向选择和辨识能力, 而且对图像边缘的表达更优于小波.结合色度饱合度亮度变换将其应用于合成孔径雷达图像和多光谱图像融合可以更好地表示图像中的有用特征.首先对多光谱图像进行色度饱合度亮度变换, 得到亮度分量I, 对雷达图像和I分量进行曲波变换得到粗尺度系数和细节尺度系数;将雷达图像的粗尺度系数和细节尺度系数进行叠加, 计算归一化的曲波系数直方图, 定义边缘有效因子, 利用合成孔径雷达图像的特征信息将曲波变换系数分为均匀区、非均匀区和亮点目标区.然后采用相应的融合规则对融合图像的粗尺度系数进行处理, 对细节尺度系数采用简单的直接取大方法, 逆变换后得到新的亮度分量.用新的亮度分量替代原亮度分量进行逆色度饱合度亮度变换得到最终融合结果, 利用统计类指标对融合结果进行评价.实验结果表明, 该方法在保持光谱信息和提高空间分辨率上都有较好的效果.

Curvelet transform, as a method of multiscale geometric analysis, is more suitable for image processing than wavelet, and more appropriate for analyzing the image edge characteristics of curve and line, which has better approximation precision and sparsity description. In this paper, the methods of integrating SAR image and MS image are proposed based on curvelet transform. SAR image and I image, which is given by a linear HSI transform are given by curvelet transform to obtain coarse coefficients and detail coefficients. The new coarse coefficients are obtained by using edge information and features of SAR image. The detail coefficients are dealt with traditional method that gets max of detail coefficients both I and SAR image.Then, the inverse curvelet transform gets the new intensity I image. Finally degree of distortion and space frequency are used to evaluate the result. The results of experiment indicate that the method excels those of based on HSI or curvelet transform in preserving spectral information and enhancing resolution.