Parameters optimization of the novel probabilistic algorithm for improving spatial resolution of multispectral satellite images
- 51 Downloads
A probabilistic method for improving the spatial resolution of multispectral space images using a reference image is proposed. The developed method calculates the mathematical expectation of pixel brightness in different channels of an improved multispectral image based on the probabilistic characteristics of the pixel neighborhood on the multispectral image and the overall brightness intensity of the panchromatic image at that point. The applicability of different metrics for evaluating the quality of the spatial resolution of satellite images is analyzed. A set of the most adequate quality evaluation metrics is used. An optimization procedure is developed to adjust the parameters of the proposed probabilistic resolution improvement method. The results of testing the method on the multi-spectral images obtained from different satellites in different spatial resolutions are presented. The efficiency of the algorithm is tested at different magnification scales. A comparative analysis of the results of the proposed method with similar approaches is conducted.
Unable to display preview. Download preview PDF.
- 1.Aerospace Monitoring of Objects of Oil and Gas Facilities, Ed. by V. G. Bondur (Nauchnyi Mir, Moscow, 2012) [in Russian].Google Scholar
- 2.A. Murynin, K. Gorokhovskiy, V. Bondur, and V. Ignatiev, “Analysis of large long-term remote sensing image sequence for agricultural yield forecasting,” in Proceedings of the 4th International Workshop on Image Mining, Theory and Applications IMTA-4 2013, with VISIGRAPP 2013, Barcelona, Spain (SCITERGRASS, Portugal, 2013), pp. 48–55.Google Scholar
- 5.A. A. Ishutin, I. S. Kikin, G. G. Sebryakov, and V. N. Soshnikov, “Algorithms for the detection, localization, and recognition of electro-optical images of the group of isolated ground locations for inertial-sighting systems of navigation and guidance of aircraft,” J. Comput. Syst. Sci. Int. 55, 242 (2016).CrossRefGoogle Scholar
- 7.V. G. Bondur, A. B. Murynin, and V. Yu. Ignat’ev, “Parameters optimization in the problem of sea-wave spectra recovery by airspace images,” Mashin. Obuchen. Anal. Dannykh 2, 218–230 (2016).Google Scholar
- 8.A. A. Gurchenkov, V. G. Bochkareva, A. B. Murynin, and A. N. Trekin, “Image quality improvement by method of spatial spectrum extrapolation,” Vestn. MGTU Baumana, Ser.: Estestv. Nauki, No. 2 (65), 91–102 (2016).Google Scholar
- 10.P. Milanfar, Super-Resolution Imaging (CRC, Boca Raton, London, New York, 2011), p. 473.Google Scholar
- 11.G. Vivone, L. Alparone, J. Chanussot, M. dalla Mura, A. Garzelli, G. Licciardi, R. Restaino, and L. Wald, “A critical comparison among pansharpening algorithms,” IEEE Trans. Geosci. Remote Sens. 53 (5) (2015).Google Scholar
- 14.P. S. Chavez, Jr.,_S. C. Sides, and A. J. Anderson, “Comparison of three different methods to merge multiresolution and multispectral data: landsat TM and SPOT panchromatic,” Photogramm. Eng. Remote Sens. 57, 295–303 (1991).Google Scholar
- 20.B. Owen Art, Monte Carlo Theory, Methods and Examples. http://statweb.stanford.edu/~owen/mc/Ch-varis.pdf.Google Scholar