摘要
针对主成分分析(PCA)融合算法的不足和最小噪声分离(MNF)变换的优势,以IKONOS新型高分辨率观测卫星提供的全色和多光谱数据为实验数据,提出了基于最小噪声分离变换的遥感影像融合方法,并与其它融合方法进行定量和视觉比较,结果表明该方法能得到更好的融合效果。
The Principal Component Analysis （PCA） image fusion method has been used widely in recent years. However, without considering the effects of noise on the fusion image, its application is only limited to the fusion region. The Minimum Noise Fraction （MNF） transform is a self - contained component analysis method which considers the effects of noise on the fusion image. This technique is employed in such fields as the determination of the inherent dimensionality of image data and segregation of noise in the data ; nevertheless, it is not applied to image fusion nowadays. Therefore, in view of the defectiveness of the PCA image fusion method and the superiority of the MNF transformation, the authors put forward a new MNF transform Remote Sensing fusion method in which both IKONOS multi - spectral image and panchromatic image are used. Visual and quantitative comparison demonstrates that this technique is better than other fusion methods.
出处
《国土资源遥感》
CSCD
2007年第2期53-55,共3页
Remote Sensing for Land & Resources
基金
地理空间信息工程国家测绘局重点实验室基金项目(B2524)
关键词
影像融合
主成分分析变换
最小噪声分离变换
HIS变换
Image fusion
PCA （ Principal Component Analysis）
MNF （ Minimum Noise Fraction）
HIS （ Hue Intensity Saturate）
