An investigation of efficiency of fusion methods of scanner aerospace multispectral images

1Hnatushenko, VV, 2Kavats, OO, 3Makarov, AL, 1Brazhnik, DP
1Oles Honchar Dnipropetrovsk National University, Dnipropetrovsk, Ukraine
2National metallurgical academy of Ukraine, Dnipropetrovsk, Ukraine
3Yangel Yuzhnoye State Design Office, Dnipropetrovsk, Ukraine
Kosm. nauka tehnol. 2014, 20 ;(5):50–54
Publication Language: Ukrainian

We study fusion methods allowing one most effectively to improve information content of multispectral aerospace high spatial resolution images with minimal color distortion. Our results indicate that the synergistic processing of multispectral data with the use of the proposed information technology on the basis of ICA- and wavelet transforms gives a better outcome as compared to the classical fusion methods. The synthesized image has some improved performance without spectral distortion

Keywords: fusion methods, multispectral aerospace resolution images, wavelet transforms

1. Gnatushenko V. V., Kavac O. O. Information technology of increase of the spatial resolution of digital satellite images based on the ISA- and wavelet transforms. Visnyk Nat. Univ "Lviv Polytechnic".  Computer Science and Information Technology.  No.771,  28—32 (2013). [in Ukrainian].
2. Kavac O. O., Gnatushenko V. V., Safarov O. O. The influence of the wavelets characteristics on the effectivity of association photogrammetric images. Proceedings of the. Tavriya State. Agrotechnical University.  Applied geometry and engineering graphics. Issue 4, Vol. 56, 33—40 (2013) [in Ukrainian].
3. Akula R., Gupta R., Devi M. R. V.An efficient PAN sharpening technique by merging two hybrid approaches.  Procedia Eng.  30, 535—541 (2012).
4. Blum R. S., Liu Z. Multi-sensor image fusion and its applications.  512 p. (CRC Press, Taylor & Francis Group, NW, 2006).
5. Chen F., et al. Fusion of remote sensing images using improved ICA mergers based on wavelet decomposition.  Procedia Eng.  29,  2938—2943 (2012).
6. Heng Chu, Weile Zhu. Fusion of IKONOS satellite imagery using IHS transform and local variation.  IEEE Trans. Geosci. and Remote Sens. 5(4) (2008).
7. Hnatushenko V., Safarov A. Computer technology more informative multispectral images of the earth surface.  Appl. Geometry and Engineering Graphics,  Vol. 89, 140—144 (KNUBA, Kyiv, 2012).
8. Li S. Multisensor remote sensing image fusion using stationary wavelet transform: effects of basis and decomposition level.  Int. J. Wavelets Multiresolut. Inform. Process.  6(1), 37—50 (2008).
9. Nirmala D. E., Paul B. S., Vaidehi V. A novel multimodal image fusion method using shift invariant discrete wavelet transform and support vector machines.  Proceedings of the International Conference on Recent Trends in Information Technology, Jun. 3—5, 2011.  Р. 932—937 (IEEE Xplore Press, Chennai, Tamil Nadu, 2011)
10. Pohl C., Van Genderen J. L. Multisensor image fusion in remote sensing: concepts, methods and applications.  Int. J. Remote Sens.  19 (5), 823—854 (1998).
11. Schowengerdt R. Remote sensing: Models and methods for image processing.  (Acad. Press, New York, 2007).
12. Wang Z. J., Ziou D., Armenakis C. A. Comparative analysis of image fusion methods.  IEEE Trans. Geosci. and Remote Sens.  43(6), 1391—1402 (2005).