结合自适应PCNN的非下采样剪切波遥感影像融合  

Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN

作　　者：成飞飞 付志涛 黄亮[1,2] 陈朋弟 黄琨 CHENG Feifei;FU Zhitao;HUANG Liang;CHEN Pengdi;HUANG Kun(Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China;Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China)

机构地区：[1]昆明理工大学国土资源工程学院,云南昆明650093 [2]云南省高校高原山区空间信息测绘技术应用工程研究中心,云南昆明650093

出　　处：《测绘学报》2021年第10期1380-1389,共10页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(41961053);云南省科技厅基础研究计划面上项目(202101AT070102);昆明理工大学自然科学研究基金省级人培项目(KKSY201921019)。

摘　　要：为解决全色与多光谱遥感影像融合中脉冲耦合神经网络参数不能自适应调节问题,提出一种基于参数自适应脉冲耦合神经网络模型(PA-PCNN)和保持能量属性(EA)融合策略相结合的非下采样剪切波变换(NSST)的遥感影像融合方法:①通过提取多光谱影像YUV颜色空间变换的Y亮度分量并与全色影像进行NSST变换,获得高频系数和低频系数。②针对低频子带系数,采用EA法进行融合;针对高频子带系数,通过PA-PCNN模型得到的最优参数,以确定最优的PCNN模型,进而实现高频子带系数的融合。③将NSST和YUV进行逆变换得到融合影像。本文选取空间频率、相对无量纲全局误差、相关系数、视觉信息保真度、基于梯度的融合性能和结构相似度测量等6种客观评价指标对融合影像的光谱和空间细节评价,利用多组不同分辨率全色和多光谱遥感影像,通过与4种融合方法对比验证,结果表明本文方法在视觉感知和客观评价方面总体优于其他全色与多光谱遥感影像融合方法。In order to solve the problem that the parameters of pulse-coupled neural network can’t be adjusted adaptively in pan-sharpening image fusion,a non-subsampled shearlet transform remote sensing image fusion method based on the combination of parametric-adaptive pulse coupled neural network model and energy-attributing fusion strategy is proposed.First,the high and low frequency coefficients are obtained by extracting the Y luminance component of the multispectral image YUV color space transform and transforming it with the panchromatic image.Then,aiming at the low-frequency sub-band coefficients are fused by the EA method,the high-frequency sub-band coefficients are obtained by the PA-PCNN model to determine the optimal PCNN model,and then the high-frequency sub-band coefficients are fused;finally,the fusion image is obtained by inverse transformation of NSST and YUV.In this paper,six objective quality indexes,such as spatial frequency,relative dimensionless global error,ERGAS,correlation coefficient,visual information fidelity for fusion,gradient-based fusion performance and structural similarity index,are selected to evaluate the spectral and spatial detail information of the fused images,compared with SE,DGIF,COF and PA-PCNN fusion methods,the proposed method is validated by using multiple sets of high-and low-resolution panchromatic and multispectral remote sensing images,the results show that this method is generally superior to the traditional fusion method of panchromatic and multispectral remote sensing images in objective evaluation and visual perception.

关 键 词：影像融合 非下采样剪切波变换 脉冲耦合神经网络 全色影像 多光谱影像 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] P237[自动化与计算机技术—控制科学与工程]