Author(s)

Bing Li¹, Yong Xian¹, Daqiao Zhang¹, Juan Su¹, Xiaoxiang Hu², Weilin Guo³

¹Xi’an High-Tech Research Institute, Xi’an, China.
²School of Automation, Northwestern Polytechnical University, Xi’an, China.
³Xi’an Satellite Control Center, Xi’an, China.

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.

Multi-Sensor Image Fusion, Fusion Strategy, Feature Enhancement, Fusion Performance Assessment

Li, B. , Xian, Y. , Zhang, D. , Su, J. , Hu, X. and Guo, W. (2021) Multi-Sensor Image Fusion: A Survey of the State of the Art. Journal of Computer and Communications, 9, 73-108. doi: 10.4236/jcc.2021.96005.