Analysis and Evaluation of IKONOS Image Fusion Algorithm Based on Land Cover Classification

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 (MLC) 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.