基于非下采样轮廓波变换耦合对比度特征的遥感图像融合算法

Remote sensing image fusion algorithm based on nonsubsampled contourlet transform and contrast characteristics

为了解决当前较多遥感图像融合方法忽略了图像的对比度特征,使得融合图像存在振铃效应等问题,设计了基于非下采样轮廓波变换耦合对比度特征的遥感图像融合算法。引入HSV(hue,saturation,value)色彩模型,提取出多光谱(MS)图像的V因子。借助非下采样轮廓波变换(NSCT),计算出V因子与全色(PAN)图像的不同系数。随后,利用傅里叶变换来求取图像的显著性因子,并将其与图像的区域能量特征相结合,形成低频系数融合规则,实现低频信息的融合。通过利用图像的标准差信息对图像的对比度特征进行度量,并将其与图像的平均梯度信息相结合,形成高频系数融合规则,实现高频信息的融合。最后,通过逆NSCT对其进行重构,以更新V因子。将更新后的V因子,联合MS图像的H因子和S因子,通过逆HSV色彩模型进行重构,得到融合结果。通过实验发现,较当前遥感图像融合技术而言,所提算法的融合图像具备更高的光谱相关系数值以及信息熵值。

In order to solve the problem as ringing effect induced by neglect of the image contrast feature in many current remote sensing image fusion methods, this paper uses the standard deviation information of the image to measure the contrast characteristics of the image, and then realizes image fusion. Hue, saturation, value(HSV) color model is introduced to extract V factor of multispectral image. With the help of NSCT, the different coefficients of V factor and panchromatic image are calculated. Then, the saliency factor of the image is obtained by Fourier transform, and combined with the regional energy characteristics of the image to form the fusion rules of low-frequency coefficients, so as to realize the fusion of low-frequency information. By using the standard deviation information of the image to measure the contrast characteristics of the image, and combining it with the average gradient information of the image, the fusion rules of high frequency coefficients are formed to realize the fusion of high frequency information. Finally, it is reconstructed by inverse NSCT to update the V factor. The updated V factor, combined with H factor and S factor of MS image, is reconstructed by inverse HSV color model, and the result is concordant. The experiments show that compared with the current remote sensing image fusion technology, this algorithm has higher spectral correlation coefficient and information entropy.