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Satellite payloads provide data covering different portions of the electromagnetic spectrum at different spatial, spectral, and temporal resolutions. To fully exploit satellite data from multiple sources, advanced analytical and numerical image fusion techniques have been developed. Fused images can provide increased interpretation capabilities and more-reliable results because data with different characteristics is combined. The fused images contain information with better spatial, spectral, and temporal resolution and therefore give a more-complete view of the observed objects.
The aim of image fusion is to integrate data of the scene from different sources in order to obtain more information than can be derived from the data of a single sensor alone. A good example is the fusion of multispectral images acquired by sensors sensitive to VNIR with images from active synthetic aperture radars (SARs). The information contained in VNIR imagery depends on the multispectral reflectivity of the targets in a scene illuminated by sunlight. The intensities of a SAR image depend on the characteristics of the targets as well as those of the signal itself. The fusion of this disparate data contributes to a better understanding of the objects of the scene.
The concept of image fusion goes back to the earlier 1960s, with the search for practical methods of merging images from various sensors to provide a composite image that could be used to better identify natural and synthetic objects. Terms such as combination, merging, integration, synergy, and several others that express more or less the same concept have since appeared in the literature.
A broader view of image fusion describes it as a group of methods and approaches that use multisource data of different natures to increase the quality of information contained in the data. Data integration is another term used for image fusion algorithms. This term is often used in the context of geographical information systems.
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