Each sensor has a particular mission and application, and it is often desirable for us to have spectral and spatial resolution in an image at the same time, since the amount of information in a picture is limited by the spatial and spectral resolution of the imaging system, and most of common imaging systems offer one of these two features, such as the tm, spot, etc. imaging systems. Of course, along with these systems, there are other systems that enjoy both good spatial resolution and considerable spectrum capabilities, like IKonos Kfa, Kvr, etc.)
To overcome the problem of systems of the first type, we use different image processing methods with which we will have both of the features (spatial and spectral resolution) in the image, an operation which is called "Image Fusion." In general, Image Fusion is more convenient and economical than the design and construction of an advanced sensor that has both spatial and spectral resolution.
The multi-spectral images provide the necessary information for interpretation of mapping land cover, but their spatial resolution is not enough. Therefore, in order to increase spatial resolution, using various methods of Image Fusion, multi-spectral images are combined with high resolution images so that we can find out more information about our surroundings and capture more details. These techniques are also known as sharpening techniques. Based on the definition of Image Fusion, EARSEL it is a tool for data that come from various sources with the aim of obtaining high-quality information.
Here it is important to note that images with different resolutions may not be simultaneously captured and changes in atmospheric light properties cause damage to composite images, and this damage is seen in Landscape, which contains smaller components (details).