基于信息量制约的遥感图像融合算法

Remote sensing image fusion algorithm based on information constraint

为了改善遥感图像的融合质量,制定了信息量制约法则用于获取融合遥感图像。利用亮度-色调-饱和度(IHS)变换从多光谱(MS)图像中获取亮度(I)分量。基于非下采样剪切波变换(NSST),获取I分量与全色(PAN)图像完成分解,得到I分量与PAN图像的高低频系数。最后,利用低频系数的均值以及区域能量信息,建立信息量制约法则,分析不同低频系数对融合系数的影响程度,设计不同的融合方法对这些低频系数进行融合。采用改进的平均梯度度量模型完成高频系数融合。通过NSST逆变换对计算融合后的高低频系数,输出新的亮度分量,将其与原始的色调(H)、饱和度(S)分量组合,通过IHS逆变换,获取融合结果。实验数据显示,相对已有的融合算法,所提算法的融合图像所含的信息量更为丰富,且光谱扭曲度更小。

In order to improve the fusion quality of remote sensing image,a remote sensing image fusion algorithm based on non-downsampling shear wave transform coupled information quantity restriction rule is proposed.Multi-Spectral(MS)images are decomposed by using Intensity-Hue-Saturation(IHS)transformation to extract the brightness(I)component of MS images.Then,based on nondownsampling shear wave transform,I component and PANchromatic(PAN)image are decomposed to obtain high and low frequency coefficients.Finally,using the mean value of low-frequency coefficients and regional energy information,the information quantity restriction rule is established,and the influence degree of different low-frequency coefficients on fusion coefficients is analyzed.Different fusion methods are designed to fuse these low-frequency coefficients.The improved average gradient measurement model is adopted to finish the fusion of high frequency coefficients.The fused high and low frequency coefficients are computed by the inverse transformation of Non-Subsampled Shearlet Transform(NSST),and new brightness components are output.The fused remote sensing images are formed by combining them with the original Hue and Saturation components through the inverse transformation of IHS.The experimental results show that compared with the existing fusion scheme,the fusion image of the proposed algorithm contains more information and less spectral distortion.