| 摘要: |
| 在遥感应用研究中,数据融合技术有着非常广泛的应用.主分量分析方法(principalcomponentanalysis,简称PCA)是一种经典的遥感数据融合技术,在主分量分析方法的基础上,将小波变换与其结合起来,提出了一种新的基于小波叠加的PCA融合算法(addingwaveletcoefficientsprincipalcomponentanalysis,简称AWPCA).实验证明,与原来的PCA和IHS方法相比,基于小波叠加的PCA融合算法进一步提高了融合信息的质量,并能应用于其他需要高分辨率图像的场合中. |
| 关键词: 数据融合 小波变换 主分量变换(principal component analysis) AWPCA(addingwavelet coefficients principal component analysis) |
| DOI： |
| 分类号: |
| 基金项目:国家“九五”重点科技攻关资助项目(96-B02-01-05) |
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| A New Data Fusion Algorithm for Improving Remote Sensing Images Resolution |
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CHEN Hao,YU Neng hai,LIU Zheng kai,ZHANG Rong
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| Abstract: |
| Data fusion has been widely applied in the remote sensing research field. Principal component analysis (PCA) is one of the standard methods for data fusion. In this paper, a new algorithm--adding wavelet coefficients principal component analysis (AWPCA) is presented, which is based on principal component analysis (PCA) and is gotten from combining PCA and wavelet transform. The experimental results demonstrate that the higher quality image is obtained by AWPCA than by IHS and PCA mergers. AWPCA can be also applied in other fields where the high-resolution image is required. |
| Key words: data fusion wavelet transform PCA （principal component analysis） AWPCA （adding wavelet coefficients principal component analysis） |
