Comparison of nine fusion techniques for very high resolution data

The term image fusion covers multiple techniques used to combine the geometric detail of a high-resolution panchromatic image and the color information of a low-resolution multispectral image to produce a final image with the highest possible spatial information content while still preserving good spectral information quality. During the lost twenty years, many methods such as Principal Component Analysis (PCA), Multiplicative Transform, Brovey Transform, and IHS Transform have been developed producing good quality fused images. Despite the quite good visual results, many research papers have reported the limitations of the above fusion techniques. The most significant problem is color distortion. Another common problem is that the fusion quality often depends upon the operator's fusion experience and upon the data set being fused. In this study, we compare the efficiency of nine fusion techniques and more specifically the efficiency of IHs, Modified IHS, PCA, Pansharp, Wavelet, LMM (Local Mean Matching), LMVM (Local Mean and Variance Matching), Brovey, and Multiplicative fusion techniques for the fusion of QuickBird data. The suitability of these fusion techniques for various applications depends on the spectral and spatial quality of the fused images. In order to quantitatively measure the quality of the fused images, we have made the following controls. First, we have examined the visual qualitative result. Then, we examined the correlation between the original multispectral and the fused images and all the statistical parameters of the histograms of the various frequency bands. Finally, we performed an unsupervised classification, and we compared the resulting images. All the fusion techniques improve the resolution and the visual result. The resampling method practically has no effect on the final visual result. The LMVM, the LMM, the Ponsharp, and the Wavelet merging technique do not change the statistical parameters of the original images. The Modified IHs provokes minor changes to the statistical parameters than the classical IHs or than the PCA. After all the controls, the LMVM, the LMM, the Pansharp, and the Modified IHs algorithm seem to gather the more advantages in fusion panchromatic and multispectral data,