Assessment and Evaluation of Different Data Fusion Techniques
Data fusion is a formal framework for combining and utilizing data originating from different sources. It aims at obtaining information of greater quality depending upon the application. There are many data fusion techniques that can be used to produce high-resolution multispectral images from a high-resolution PANchromatic (PAN) image and low-resolution MultiSpectral (MS) images, including but not limited to, modified Intensity - hue - saturation, Brovey transform, principal component analysis, multiplicative transform, wavelet resolution merge, high-pass filtering and Ehlers fusion. One of the major problems associated with a data fusion technique is how to assess the quality of the fused (spatially enhanced) MS images. This paper represents a comprehensive analysis and evaluation of the most commonly used data fusion techniques.