Concepts of Image Fusion in Remote Sensing Applications

Earth observation satellites provide data covering different parts of the electromagnetic spectrum at different spatial, spectral, and temporal resolutions. To utilize these different types of image data effectively, a number of image fusion techniques have been developed. Image fusion is defined as "the set of methods, tools, and means of using data from two or more different images to improve the quality of the information" [1]. The fused image has rich information that will improve the performance of image analysis algorithms. This increase in quality of the information leads to better processing (ex: classification, segmentation) accuracies compared to using the information from one type of data alone. In this paper pixel level and feature level image fusion are applied for the classification of a co-registered QuickBird multispectral and panchromatic images.