Multispectral monitoring systems are becoming more widely used in which the fusion of information obtained in the form of different spectral images is applied. Direct fusion of such images is impossible in connection with the presence of relative images spatial deformations and in connection with the different sensor resolution that form these images. Before fusion of the images, you must bind them to a single coordinate system.
The development of a method that, in contrast to the classical one, allows matching of images formed by sensors of different physical nature in the context of their integration in multispectral monitoring systems is conducted. Reasonably experimentally that for matching of the different spectral images is impractical to use the classic methods used in matching of monospectral images, in particular, least squares method and the normalized correlation coefficient, as appropriate to use methods invariant to brightness conversion. This is the method that uses the maximum of the function of mutual information between images as a criterion for the similarity of the images.
The simulation results show that the method based on the mutual information function gives better results of the different spectral images matching than the other considered methods. The position of the maximum of the mutual information function indicates the optimal displacement between the images despite the different intensity distribution on the images from the thermal imaging camera and the television camera, even with poor visibility in one of the channels.
1. Ivanov, E. L. and Smagin, M. S. (2006), “Merging images in a multichannel terrain observation system”, Sensors and systems, no. 11, pp. 6–9. 2. “E2VS Displays Potential as Breakthrough Product”, Aviation week network, October 2015,available at: http://aviationweek.com/nbaa-2015/e2vs-displays-potential-breakthrough-p... (Accessed 10 April 2017). 3. Canga, E. F. (2002), Image fusion. Project report for the degree of Meng. in electrical and electronic engineering, University of Bath. 4. Stathaki, Т. (2008), Image fusion: Algorithm and Applications, Elsevier, 519 p. 5. Blum, R. S. and Liu, Z. (2006), “Multi-Sensor Image Fusion and Its Applications”, Signal Processing and Communications, pp. 40–42. 6. Aksenov, O. Yu. (2005), “Image combination”, DSP, no. 3, pp. 51–55. 7. Voitov, V. A., Golitsyn, A. V., Degtyarev, E. V., Zhuravlev, P. V., Zhurov, G. E. and Shlishevsky, V. B. (2009), “The way of a single information field formation”, Optical journal, vol. 76, no. 12, pp. 84–87. 8. Zubkov, A. M., Lazko, L. V., Mymrikov, D. O., and Prudyus, I. N (2011), “Integrated monitoring systems with information integration of sensors in different parts of the spectrum of electromagnetic waves”, Applied Radio Electronics. Status and prospects of development: 4th intern. Radio electronics forum, Oct. 18–21. 2011. pp. 170–173. 9. Dushepa, V. A. and Uss, M. L. (2011), " Comparative analysis of sub-pixel algorithms with image matching “, Radio electronic and computer systems, no 4, pp. 41–51. 10. Gruzman, I. S., Kirichuk, V. S., Kosykh, V. P., Peretyagin, G. I. and Spector, A. A. (2002), Digital Image Processing in Information Systems: Tutorial, Novosibirsk: Publishing house of the National Technical University, 352 p. 11. Krasil’shchikov, M. N. and Sebryakov, G. G. (2009), Modern information technologies in the tasks of navigation and guidance of unmanned maneuverable aircraft, Moscow: Fizmatlit Publishing House, 554 p. 12. Lewis, J. P. (1995), Fast Normalized Cross-Correlation, Industrial Light & Magic. 13. Raghavendra, R., Venkatesh, S., Raja, K. B., Cheikh, F. A. and Busch, C. (2016), “Mutual Information Based Multispectral Image Fusion for Improved Face Recognition”, 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, pp. 62–68. 14. Pradhan, S. and Patra, D. (2016), “Enhanced mutual information based medical image registration”, IET Image Processing, vol. 10, no. 5, pp. 418–427. 15. Sahoo P. K. and Pati U. C. (2015),"Image registration using mutual information with correlation for medical image", 2015 Global Conference on Communication Technologies (GCCT), Thuckalay, pp. 34–38.