吉林大学学报（理学版）2018，Vol.56Issue(3)：617-624,8.DOI:10.13413/j.cnki.jdxblxb.2018.03.25
基于加权小波分析的遥感图像融合算法
Remote Sensing Images Fusion Algorithm Based on Weighted Wavelet Analysis
摘要
Abstract
We proposed a remote sensing images fusion algorithm based on weighted wavelet analysis . Firstly ,we extracted the illumination intensity component of multispectral images in the illumination chroma saturation space , and made a principal component analysis of the illumination intensity component to get the corrected illumination intensity .Secondly ,the weighted fusion was carried out by corrected illumination intensity and wavelet analysis for the high frequency region .Finally ,the final fusion results of remote sensing images were obtained by inverse transform of wavelet analysis and illumination chroma saturation space . This algorithm effectively solved the problems of block blurred phenomenon caused by the way of wavelet analysis to discard low frequency components and distortion of information domain produced by principal component analysis in the process of image fusion with low spatial resolution and high spatial resolution .We carried out simulation experiments on the fusion of remote sensing image in different scenes . The results show that the proposed algorithm can solve some common problems such as blurring of edge after image fusion and fuzzy block shadow appearing in fusion result . It is greatly improved in clarity , texture details and authenticity ,and can adjust the appropriate weighting coefficient for different fusion needs ,so that the fusion of remote sensing images can achieve the best effect .The weighted wavelet analysis further improves the effect of remote sensing image fusion ,not only fully expresses the details of various remote sensing images ,but also preserves the orginal spectral information better .关键词
HSI颜色空间/主成分分析/离散小波分析/遥感图像融合Key words
HSI color space/principal component analysis/discrete wavelet analysis/remote sensing image fusion分类
信息技术与安全科学引用本文复制引用
莫才健,田健榕,武锋强,陈莉,邹强..基于加权小波分析的遥感图像融合算法[J].吉林大学学报（理学版）,2018,56(3):617-624,8.基金项目
国家自然科学基金重点项目基金(批准号:41401598)、四川省教育厅科研项目(批准号:18ZA0489)和西南科技大学博士基金(批准号:14ZX7128). （批准号:41401598）
