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 lowresolution multispectral image to produce a final image with the highest possible spatial information content while still preserving good spectral information quality. During the last 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 Pansharp, 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. Introduction Almost all the high-resolution (SPOT, Landsat, IRS, Ikonos, QuickBird, and Orbview) collect a high spatial resolution panchromatic (PAN) image and multiple (usually four) multispectral (MS) images with significant lower spatial Comparison of Nine Fusion Techniques for Very High Resolution Data Konstantinos G. Nikolakopoulos resolution. The PAN images are characterized by very high spatial information content well-suited for intermediate scale mapping applications and urban analysis. The MS images provide the essential spectral information for smaller scale thematic mapping applications such as land-use surveys. It is of interest to note that most satellites do not collect high-resolution MS images directly, to meet this requirement for high-spatial and high-spectral resolutions. There is a limitation to the data volume that a satellite sensor can store on board and then transmit to a ground receiving station. Usually the size of the PAN image is many times larger than the size of the MS images. The size of the PAN of Landsat ETM is four times greater than the size of an ETM MS image. The PAN image for Ikonos, QuickBird, SPOT5, and Orbview is sixteen times larger than the respective MS images. As a result, if a sensor collected high-resolution multispectral data, it could acquire fewer images during every pass. Considering these limitations, it is clear that the most effective solution for providing high-spatial-resolution and high-spectral-resolution remote sensing images is to develop effective image fusion techniques. The principal interest of fusing multi-resolution image data is to create composite images of enhanced interpretability (Welch and Ehlers, 1987; Kaczynski et al., 1995). The images should have the highest possible spatial information content while still preserving good spectral information quality (Cliché et al., 1985). Some authors stress the idea that the merging method used should not distort the spectral characteristics of the original MS data, ensuring that targets, which are spectrally separable in the original data, are still separable in the merged data set (Chavez et al., 1991). Such products not only allow a more accurate delineation of ground features, making them more useful for various applications (Vrabel, 1996), but also are more easily interpretable in terms of their original spectral signatures. Garguet-Duport et al. (1996) demonstrated that spectral information preservation is particularly well suited in the case of vegetation analysis, and its usefulness in urban mapping applications. Going one-step further, some authors even suggest that fused products with maximal spectral information preservation could ideally simulate MS images acquired at higher spatial resolutions (Vrabel, 1996; Wald et al., 1997). Different merging methods have been proposed in the literature; using Principal Component Analysis (Chavez et al., 1991), Intensity-Hue-Saturation (IHS) transforms (Haydn et al., 1982; Carper et al., 1990), Brovey Transform PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING May 2008 647 University of Athens, Department of Geology & Geoenvironment, Remote Sensing Laboratory, 1, Iroon Polytechniou Street, 15127 Melissia, Greece ([email protected]) Photogrammetric Engineering & Remote Sensing Vol. 74, No. 5, May 2008, pp. 647–659. 0099-1112/08/7405–0647/$3.00/0 © 2008 American Society for Photogrammetry and Remote Sensing 06-052.qxd 4/11/08 3:28 PM Page 647