Abstract
An approach to filtration of hyperspectral images corrupted by the Gaussian additive noise is proposed. The approach is based on using the property of interchannel redundancy of such images. The developed algorithm of noise filtration allows maintaining the contour and brightness portraits of objects in individual components of the hyperspectral image, in contrast to algorithms of linear component-by-component and vector filtration, as well as the algorithm of averaging over a set of components. The numerical results obtained in the study testify to the advantage provided by interchannel gradient reconstruction in terms of the accuracy of recovery of hyperspectral image components corrupted by additive noise. The efficiency of the proposed approach is demonstrated by an example of processing of real hyperspectral images.
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References
P. M. Yukhno, S. M. Ogreb, and M. V. Tishaninov, “Statistical Synthesis of a Hypersonic Detector,” Avtometriya 51 (3), 61–69 (2015) [Optoelectron., Instrum. Data Process. 51 (3), 264–271 (2015)].
A. V. Anishchenko, S. M. Ogreb, and P. M. Yukhno, “Comparative Analysis of Panchromatic and Multispectral Regimes of Detection of Three-Dimensional Objects,” Optika Atmos. Okeana 26 (8), 673–678 (2019).
S. M. Ogreb and P. M. Yukhno, “Comparative Efficiency of Methods of Cooperative Processing of Imagery Information,” Avtometriya 55 (4), 108–117 (2019) [Optoelectron., Instrum., Data Process. 55 (4), 406–413 (2019)].
Advanced Technologies of Processing of Remote Sensing Data, Ed. by V. V. Eremeev (Fizmatlit, Moscow, 2015) [in Russian].
S. M. Borzov and O. I. Potaturkin, “Spectral-Spatial Methods for Hyperspectral Image Classification,” Avtometriya 54 (6), 64–86 (2018) [Optoelectron., Instrum. Data Process. 54 (6), 582–599 (2018)].
R. C. Gonzalez and R. E. Woods, Digital Image Processing (Pearson International Edition, 2008).
T. S. Huang, J.-O. Eklund, G. J. Nussbaumer, et al., Fast Algorithms in Digital Image Processing (Radio i Svyaz, Moscow, 1984).
Yu. E. Voskoboinikov and V. G. Belyavtsev, “nonlinear Algorithms of Vector Signal Filtration,” Avtometriya, No. 5, 97–105 (1999).
E. A. Samoilin and V. V. Shipko, “Interchannel Gradient Reconstruction of Color Images Corrupted by Impulse Noise,” Avtometriya 50 (2), 22–30 (2014) [Optoelectron., Instrum. Data Process. 50 (2), 125–131 (2014)].
E. A. Samoilin and V. V. Shipko, “Investigation of Accuracy Characteristics of the Method of Interchannel Gradient Reconstruction of Digital Color Images,” Avtometriya 50 (4), 59–66 (2014) [Optoelectron., Instrum. Data Process. 50 (4), 370–376 (2014)].
E. A. Samoilin and V. V. Shipko, “Iterative Algorithms of Interchannel Gradient Reconstruction of Multicomponent Images Corrupted by Applicative Interferences,” Opt. Zh. 81 (4), 54–60 (2014)
Yu. S. Sagdullaev and S. D. Kovin, Perception and Analysis of Images with Different Spectral Characteristics (Sputnik+, Moscow, 2016) [in Russian].
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Russian Text © The Author(s), 2020, published in Avtometriya, 2020, Vol. 56, No. 1, pp. 23–32.
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Shipko, V.V. Noise Filtration in Hyperspectral Images. Optoelectron.Instrument.Proc. 56, 19–27 (2020). https://doi.org/10.3103/S8756699020010033
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DOI: https://doi.org/10.3103/S8756699020010033