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基于PCA 和NSCT 变换的遥感图像融合方法

  

  1. 北方民族大学数学与信息科学学院，宁夏 银川 750021
  • 出版日期:2017-04-30 发布日期:2017-04-28
  • 基金资助:
    国家自然科学基金项目(61440044，6110200)；北方民族大学科研项目(2014XYZ04)；北方民族大学研究生创新项目(YCX1680)

Remote Sensing Image Fusion Method Based on PCA and NSCT Transform

  1. Institute of Mathematics and Information Science, Beifang University of Nationalities, Yinchuan Ningxia 750021, China
  • Online:2017-04-30 Published:2017-04-28

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摘要： 为了改善非下采样Contourlet 变换(NSCT)在图像细节信息表达的缺失问题，提出了
一种新的基于主成分分析(PCA)和NSCT 的遥感图像融合方法。首先对低空间分辨率多光谱(MS)
图像进行PCA 变换，提取第一主分量(PC1)；其次，对PC1 和高空间分辨率全色(PAN)图像进行
NSCT 变换，对二者的低频系数采用小波变换的融合规则，高频系数采用基于区域标准差自适应
加权的融合规则；最后，经过PCA 逆变换和NSCT 逆变换得到融合图像。仿真实验结果表明，
该方法不仅有效地融合了源图像的细节信息，而且得到了较好的视觉效果和较优的评价指标。

关键词: 遥感图像融合, NSCT 变换, PCA 变换, 小波变换, 融合规则, 区域标准差自适
应加权

Abstract: In order to improve the problem of the lack of detailed information in expression of image
using non-subsampled Contourlet transform (NSCT), this paper proposes an improved method based
on principal component analysis (PCA) and NSCT transform remote sensing image fusion. Firstly,
PCA transform is applied to the low spatial resolution multi-spectral (MS) image, and then the first
principal component (PC1) is extracted. Secondly, NSCT transform is applied to the PC1 and the high
spatial resolution panchromatic (PAN) image. For the low frequency coefficients of the above two,
the rules of wavelet transform fusion are used, and for the high frequency coefficients the adaptive
weighted fusion rules based on region standard deviation are used. Finally, we get the fusion image by
using inverse NSCT transform and inverse PCA transform. The results show that the method
combines the detail information of the source image effectively, and also get better visual effect and
better evaluation index.

Key words: remote sensing image fusion, NSCT transform, PCA transform, wavelet transform, fusion
rules, region standard deviation of the adaptive weighted