高分辨率遥感影像融合研究

遥感影像融合不仅可以提高原多光谱影像的空间分辨率,更重要的是最大量地保留影像的光谱信息。为了研究适合于QuickBird遥感影像融合的融合方法,本研究应用乘法复合算法(MLT)、改进的Brovey(MB)、高通滤波(HPF)以及基于平滑滤波的亮度调节算法(SFIM)四种融合方法对QuickBird影像进行了融合试验和分析。试验区以覆盖不同土地利用类型的一小景QuickBird影像为基础。采用了均值偏差、标准差、信息熵、平均梯度和相关系数五种数字统计方法来定量地评价由以上算法产生的融合影像。分析结果表明:SFIM算法在光谱保真性、高频信息融入度、影像清晰度方面都优于其他三种方法。因此,在研究的四种方法中,SFIM算法最适合Quick-Bird影像融合。

Study on image fusion for high spatial resolution remote sensing images

Remote sensing images fusion can not only improve the spatial resolution for the original multi-spectral image,but also preserve the spectral information to a certain degree.In order to find out the fusion algorithm suited for QuickBird images fusion,four simple fusion algorithms,Multiplication(MLT),Modified Brovey(MB),High-Pass Filter(HPF) and the Smoothing Filter-based Intensity Modulation(SFIM) algorithms have been employed for result evaluation.Based on a QuickBird sub-scene covering different land use,numerical statistical methods such as bias of mean,correlation coefficient,entropy,standard deviation and average grads are used to quantitatively assess the fused images.Analysis indicates that the SFIM-fused image has the best definition as well as spectral fidelity,and is the best in high textural information absorption.Therefore it is suited for QuickBird image fusion best.