遥感影像融合方法比较研究

影像融合是提高遥感影像特征提取、分类、目标识别能力的重要手段;为比较不同融合方法的融合效果,采用基于低通滤波的IHS变换融合法、PCA变换法、高通滤波法和Gram-Schmidt变换融合法对北京某地区的SPOT-5全色影像和TM多光谱影像进行融合试验,并采用均值、标准差、信息熵和相关系数4个评价指标对结果影像从信息保留性和光谱保持性上进行评价;结果表明:在4种方法中,Gram-Schmidt变换融合法在信息量的保持上具有最大优势,光谱保持性与PCA变化法持平;高通滤波法在对信息量的保留和光谱信息保持均较好,但融合结果的亮度性最差。

Comparative Research on Remote Sensing Image Fusion Method

Image fusion is an important method for improving feature extraction, classification and objective recognition capacity of remote sensing images. In order to compare the effect of different fusion methods, this paper uses the algorithm based on HIS transform and low pass filter, PCA, High pass Filtering and Gram Schmidt to implement fusion experiment on SPOT 5 data and TM data to integrate the images in Beijing, and uses four methods such as mean value, standard deviation, information entropy and relative coefficient to evaluate the spatial information and the spectral fidelity. The results show that in the four methods, Gram Schmidt fusion method has the most advantage in reserving information and that its spectral fidelity is flat as PCA method. High pass Filtering fusion method is all good in the retention of information and spectral fidelity but its brightness of the fusion result is worst among the above four methods.