基于局部均值和标准差的Curvelet变换遥感图像融合算法

Curvelet Algorithm of the Remote Sensing Image Fusion Based on the Local Mean and Standard Deviation

为充分利用各种遥感图像的有用信息,在Curvelet变换的基础上,提出一种基于局部均值和标准差限定的遥感图像融合算法.通过Curvelet变换将同一场景下的高空间分辨率全色图像和低分辨率多光谱图像分解到不同尺度、不同方向的频带范围内,然后对低、高频系数分别采用最大值法和局部均值及标准差限定法的融合规则进行融合,最后进行Curvelet反变换得到融合图像.实验表明,基于该融合规则的Curvelet算法优于传统的遥感图像融合算法.

In order to adequately make use of all kinds of remote sensing images, based on the Curvelet transformation, a new fusion algorithm for remote sensing images is proposed by using the local mean and standard deviation. By Curvelet transform, the same scene of high spatial resolution panchromatic image and low resolution multispectral image are decomposed into different scales and different directions. And then, the Curvelet coefficients for the fused image can be obtained by using maximum value to low frequency and local mean and standard deviation to high frequency. Finally, the fused image is reconstructed by the inverse Curvelet transform. The experimental results show that the proposed fusion rules of Curvelet is superior to traditional algorithm for remote sensing image fusion.