  Remote sensing image fusion is an effective way to use a large volume of data
from multisensor images. Most earth satellites such as SPOT, Landsat 7, IKONOS
and QuickBird provide both panchromatic (Pan) images at a higher spatial
resolution and multispectral (MS) images at a lower spatial resolution and many
remote sensing applications require both high spatial and high spectral
resolutions, especially for GIS based applications. An effective image fusion
technique can produce such remotely sensed images. Image fusion is the
combination of two or more different images to form a new image by using a
certain algorithm to obtain more and better information about an object or a
study area than. The image fusion is performed at three different processing
levels which are pixel level, feature level and decision level according to the
stage at which the fusion takes place. There are many image fusion methods that
can be used to produce high resolution multispectral images from a high
resolution pan image and low resolution multispectral images. This paper
explores the major remote sensing data fusion techniques at pixel level and
reviews the concept, principals, limitations and advantages for each technique.
This paper focused on traditional techniques like intensity hue-saturation-
(HIS), Brovey, principal component analysis (PCA) and Wavelet.
