航空学报 > 2009, Vol. 30 Issue (3): 490-496

残差超复数偶对分解的多光谱和全色图像融合方法

杨惠娟,张建秋,胡波   

  1. 复旦大学 电子工程系
  • 收稿日期:2007-12-19 修回日期:2008-04-07 出版日期:2009-03-25 发布日期:2009-03-25
  • 通讯作者: 张建秋

Residual Error Hypercomplex Symplectic Decomposition Approach to  Multispectral and Panchromatic Image Fusions

Yang Huijuan, Zhang Jianqiu, Hu Bo   

  1. Department of Electronic and Engineering, Fudan University
  • Received:2007-12-19 Revised:2008-04-07 Online:2009-03-25 Published:2009-03-25
  • Contact: Zhang Jianqiu

摘要:

提出一种残差超复数偶对分解的多光谱和全色图像融合方法。用超复数分别对多光谱图像和全色图像的残差图像建模,并对多光谱图像的超复数残差模型沿彩色空间的灰度轴方向分别进行超复数偶对分解,得到包含亮度信息的单部分和包含色度信息的复部分。分析表明用高分辨率全色图像的超复数残差图像来替换低分辨率多光谱图像分解后得到的复部分,可以恢复出高分辨率的多光谱图像的残差,从而实现多光谱图像和全色图像的融合。仿真结果验证了所提方法的有效性,同时验证了该方法不存在人眼可见的光谱畸变。各种现有图像融合评估方法的评估结果表明:该方法优于亮度、色度、饱和度(IHS)、主元分析(PCA)和小波变换的融合方法。

关键词: 超复数, 超复数偶对分解, 多光谱图像, 全色图像, 图像融合, 图像处理

Abstract:

A residual error hypercomplex symplectic decomposition approach to multispectral (MS) and panchromatic (PAN) image fusion is presented. The residual errors of MS and PAN images are modeled by hypercomplex respectively. Hypercomplex symplectic decomposition is applied to the hypercomplex residual error of the MS images along the gray axis of its color space. The result would be the simplex part containing the luminance information of the image and the perplex part including the chrominance information of the image. Analysis indicates that the residual error of high resolution MS images can be recovered once the simplex part of low resolution MS images is replaced by the residual error of high resolution PAN images.Consequently, the fusion of MS and PAN images can be implemented. Simulation results not only verify the effectiveness of the proposed method but also show that there is no distortion of visible spectrums in images fused by this method. Existing performance evaluations for image fusion also demonstrate that the fusion results of this approach are better than those of intensity hue saturation(IHS),principal component analysis (PCA) and wavelet-based fusion methods.

Key words: hypercomplex, hypercomplex symplectic decomposition, multispectral image, panchromatic image, image fusion, image processing

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