不同的遥感影像融合算法有不同的优点和局限性,因此难以单纯评价某种算法的优劣,融合算法的选择与研究对象和应用目的有着密切的关系.在概略介绍IHS变换、Brovey变换、PCA变换、SFIM变换及Gram-Schmidt变换5种图像融合算法原理的基础上,对IKONOS全色和多光谱数据进行像元级融合,并对融合效果进行了定性和定量评价.在此基础上,对融合影像进行最大似然法分类,利用混淆矩阵对分类结果进行精度分析,以期找出适合于地表覆盖分类的IKONOS影像融合算法.结果表明,在图像空间信息提高和光谱信息保真方面,以SFIM变换和Gram-Schmidt变换相对较好,其中Gram-Schmidt变换对图像微小细节反差的表达能力优于SFIM变换.在上述5种变换中,SFIM及Gram-Schmidt变换后融合影像地表覆盖分类精度较高,总体精度均超过98％,Gram-Schmidt变换的分类精度略高于SFIM变换,IHS变换后融合影像的分类精度最低,其总体精度和Kappa系数分别为83.14％和0.76.因此,利用Gram -Schmidt变换和SFIM变换得到的IKONOS融合影像更有利于提高地表覆盖分类精度.%Different fusion algorithm has its own advantages and limitations, so it is very difficult to simply evaluate the good points and bad points of the fusion algorithm. Whether an algorithm was selected to fuse object images was also depended upon the sensor types and special research purposes.Firstly, five fusion methods, i. e. IHS, Brovey, PCA, SFIM and Gram-Schmidt, were briefly described in the paper. And then visual judgment and quantitative statistical parameters were used to assess the five algorithms. Finally, in order to determine which one is the best suitable fusion method for land cover classification of IKONOS image, the maximum likelihood classification ( MIX ) was applied using the above five fusion images. The results showed that the fusion effect of SFIM transform and Gram-Schmidt transform were better than the other three image fusion methods in spatial details improvement and spectral information fidelity, and Gram-Schmidt technique was superior to SFIM transform in the aspect of expressing image details.The classification accuracy of the fused image using Gram-Schmidt and SFIM algorithms was higher than that of the other three image fusion methods, and the overall accuracy was greater than 98%. The IHS-fused image classification accuracy was the lowest, the overall accuracy and kappa coefficient were 83. 14% and 0, 76, respectively. Thus the IKONOS fusion images obtained by the Gram-Schmidt and SFIM were better for improving the land cover classification accuracy.
展开▼
