计算机应用 ›› 2015, Vol. 35 ›› Issue (7): 2015-2019.DOI: 10.11772/j.issn.1001-9081.2015.07.2015

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于改进Contourlet变换的遥感图像融合算法

陈利霞1, 邹宁2, 袁华2, 欧阳宁2   

  1. 1. 桂林电子科技大学 数学与计算科学学院, 广西 桂林 541004;
    2. 认知无线电与信息处理教育部重点实验室(桂林电子科技大学), 广西 桂林 541004
  • 收稿日期:2015-02-02 修回日期:2015-03-25 出版日期:2015-07-10 发布日期:2015-07-17
  • 通讯作者: 袁华(1975-),男,湖北宜昌人,讲师,硕士,主要研究方向:图像处理、智能图像处理,vincentzn530@sina.com
  • 作者简介:陈利霞(1979-),女,湖北黄冈人,副教授,博士,主要研究方向:数字图像处理、小波分析、偏微分方程数值解; 邹宁(1988-),男,广西柳州人,硕士研究生,主要研究方向:图像融合、多尺度分析; 欧阳宁(1972-),男,湖南宁远人,教授,博士,主要研究方向:模式识别、智能信息处理、图像信号处理。
  • 基金资助:

    国家自然科学基金资助项目(61362021);广西自然科学基金资助项目(2014GXNSFDA118035,2013GXNSFDA019030,2013GXNSFAA019331,2012GXNSFBA053014,2012GXNSFAA053231);广西科学研究与技术开发计划项目(桂科攻1348020-6;桂科能1298025-7);广西教育厅重点项目(201202ZD044,2013YB091);桂林市科技开发项目(20130105-6,20140103-5)。

Remote sensing image fusion algorithm based on modified Contourlet transform

CHEN Lixia1, ZOU Ning2, YUAN Hua2, OUYANG Ning2   

  1. 1. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin Guangxi 541004, China;
    2. Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology), Guilin Guangxi 541004, China
  • Received:2015-02-02 Revised:2015-03-25 Online:2015-07-10 Published:2015-07-17

摘要:

针对基于Contourlet变换的遥感融合图像空间分辨率较低的问题,提出了一种基于改进的Contourlet变换(MCT)的遥感图像融合方法。首先,对多光谱图像进行亮度-色调-饱和度(IHS)变换,得到其亮度、色调、饱和度三个分量;其次,取多光谱图像的亮度分量,与直方图匹配后的全色图像进行改进的Contourlet变换,分别获得低频子带系数与高频子带系数;然后,对低频子带系数采用平均法进行融合,对高频子带系数采用新改进的拉普拉斯能量和(NSML)作为融合规则进行融合;最后,把融合结果作为多光谱图像的亮度分量,通过IHS逆变换得到融合的遥感图像。将所提方法与基于主成分分析(PCA)和Shearlet的方法、基于PCA与小波的方法以及基于非下采样Contourlet变换(NSCT)的方法相比,所提方法在清晰度评价指标平均梯度上分别提高了7.3%、6.9%和3.9%。实验结果表明,所提方法提高了Contourlet变换的频率局部化特性和分解系数利用率,在保持多光谱信息的基础上,有效地提高了遥感融合图像的空间分辨率。

关键词: 图像融合, 遥感图像, 伪吉布斯现象, 轮廓波变换, 改进的拉普拉斯能量和

Abstract:

Focusing on the issue that remote sensing fusion image based on Contourlet transform has low spatial resolution, a remote sensing image fusion algorithm based on Modified Contourlet Transform (MCT) was proposed. Firstly, the multi-spectral image was decomposed into intensity component, hue component and saturation component by Intensity-Hue-Saturation (IHS) transform; secondly, Modified Contourlet decomposition was done between the intensity component and the panchromatic image after histogram matching to get low-pass subband coefficients and high-pass subbands coefficients; and then, the low-pass subband coefficients were fused by the averaging method, and the high-pass subbands coefficients were merged by Novel Sum-Modified-Laplacian (NSML). Finally, the fusion result was regarded as the intensity component of multi-spectral image, and remote sensing fusion image was obtained by inverse IHS transform. Compared with the algorithms based on Principal Components Analysis (PCA) and Shearlet, based on PCA and wavelet, based on NonSubsampled Contourlet Transform (NSCT), the average gradient that was used for evaluating image sharpness of the proposed method respectively increased by 7.3%, 6.9% and 3.9%. The experimental results show that, the proposed method enhances the frequency localization of Contourlet transform and the utilization of decomposition coefficients, and on the basis of keeping multi-spectral information, it improves the spatial resolution of remote sensing fusion image effectively.

Key words: image fusion, remote sensing image, pseudo Gibbs phenomenon, Contourlet transform, Sum-Modified-Laplacian (SML)

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