Survey of Multispectral Image Fusion Techniques in Remote Sensing Applications

1.1 Definition of image fusion With the development of multiple types of biosensors, chemical sensors, and remote sensors on board satellites, more and more data have become available for scientific researches. As the volume of data grows, so does the need to combine data gathered from different sources to extract the most useful information. Different terms such as data interpretation, combined analysis, data integrating have been used. Since early 1990’s, “Data fusion” has been adopt and widely used. The definition of data fusion/image fusion varies. For example: Data fusion is a process dealing with data and information from multiple sources to achieve refined/improved information for decision making (Hall 1992)[1]. Image fusion is the combination of two or more different images to form a new image by using a certain algorithm (Genderen and Pohl 1994 )[2]. Image fusion is the process of combining information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing. (Guest editorial of Information Fusion, 2007)[3]. Image fusion is a process of combining images, obtained by sensors of different wavelengths simultaneously viewing of the same scene, to form a composite image. The composite image is formed to improve image content and to make it easier for the user to detect, recognize, and identify targets and increase his situational awareness. 2010. (http://www.hcltech.com/aerospace-and-defense/enhanced-vision-system/). Generally speaking, in data fusion the information of a specific scene acquired by two or more sensors at the same time or separate times is combined to generate an interpretation of the scene not obtainable from a single sensor [4]. Image fusion is a component of data fusion when data type is strict to image format (Figure 1). Image fusion is an effective way for optimum utilization of large volumes of image from multiple sources. Multiple image fusion seeks to combine information from multiple sources to achieve inferences that are not feasible from a single sensor or source. It is the aim of image fusion to integrate different data in order to obtain more information than can be derived from each of the single sensor data alone (`1+1=3’)[4].