Agencies from all over the world have launched or are preparing for launching many earth observation systems of high resolution, multi-bands and multi-sensors including SPOT-5, Landsat-7, ENVISAT-1, TRMM (Tropical Rainfall Measuring Mission), EOS-AM (Earth Observing System), TERRA-MODIS (Moderate Resolution Imaging Spectroradiometer) and CBERS (Chinese Brazilian Earth Resource Satellite). Each of these images has its own characteristics which different levels of spatial or spectral details. The high spatial resolution images are required for an accurate description of shapes, features and structures whereas different objects and land surfaces are better identified if high spectral resolution images are available. Hence, the main goal of fusion data techniques is to combine the high spatial and the high spectral resolutions in order to produce synthesised images, which retains the spectral values while at the same time improving the spatial characteristics of the original multispectral image. These synthesised images should be as close as possible to reality and should simulate what a sensor having the same spectral bands but the highest spatial resolution would observe.
The combination of images with complementary information opens a broad field of applications such as in geology, agriculture, land use, forestry, change detection, etc. However, image fusion has not reached an operational status due to the difficulty of generalizing fusion techniques. In general, such technique depends on the application. The objective of this research is to study and evaluate different image fusion approach, which can be applied to integrate images from different resolutions.