关键词: 特征量积, 主成分分析（PCA）变换, 遥感图像融合, 小波变换

Abstract: This paper discusses the PCA（Principal Component Analysis） and a few remote sensing image fusion methods that combine the PCA and the WT（Wavelet Transform）.Traditional PCA algorithms loss some useful information about spectral characteristics of the first principal component of multi-spectral images，and influence largely the spectral resolution of fusion images.A remote sensing image fusion algorithm is implemented based on the feature product of the PCA and wavelet，which makes use of the time-frequency analysis features of wavelet transform and feature product that fuses the first principal component of multi-spectral images.The improved algorithm has been tested on various multi-spectral and panchromatic satellite remote sensing images for different areas.Experiment results show that the algorithm has a better fusion result from the perspective of either subjective visual effect or objective statistical data.

Key words: feature product, Principal Component Analysis（PCA） transform, remote sensing image fusion, wavelet transform