Multi-Sensor Image Fusion: A Survey of the State of the Art

Abstract

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.