DOI: 10.17586/1023-5086-2018-85-10-26-32
УДК: 528.852.1
Method for reducing the redundancy of optoelectronic remote Earth-probing data based on the restructuring of gray-scale images
Full text «Opticheskii Zhurnal»
Full text on elibrary.ru
Publication in Journal of Optical Technology
Григорьев А.Н., Дудин Е.А. Метод сокращения избыточности данных оптико-электронного дистанционного зондирования Земли на основе реструктуризации полутоновых изображений // Оптический журнал. 2018. Т. 85. № 10. С. 26–32. http://doi.org/10.17586/1023-5086-2018-85-10-26-32
Grigoriev A.N., Dudin E.A. Method for reducing the redundancy of optoelectronic remote Earth-probing data based on the restructuring of gray-scale images [in Russian] // Opticheskii Zhurnal. 2018. V. 85. № 10. P. 26–32. http://doi.org/10.17586/1023-5086-2018-85-10-26-32
A. N. Grigor’ev and E. A. Dudin, "Method for reducing the redundancy of optoelectronic remote Earth-probing data based on the restructuring of gray-scale images," Journal of Optical Technology. 85(10), 618-623 (2018). https://doi.org/10.1364/JOT.85.000618
This paper proposes a redundancy-reduction (compression) method for the remote Earth-probing data of gray-scale images. This method is based on an image-restructuring prototype and principal component analysis. A technique is developed for analyzing the quality of the redundancy-reduction method for remote-probing data, based on which the method developed here is compared with existing data-compression standards used with remote probing. It is shown that the results of this data-redundancy-reduction method have better quality than those of analogous methods. Particular recommendations are formulated for using this method.
remote probing, optoelectronic imaging, image restructuring, redundancy reduction, data compression
OCIS codes: 100.2000, 280.4991
References:1. D. Vatolin, A. Ratushnyak, M. Smirnov, and V. Yukin, Data-Compression Methods: Arrangement of Archivers, Image Compression, and Video (DIALOG-MIFI, Moscow, 2002).
2. E. A. Dudin, B. V. Titkov, and A. I. Altukhov, “Technology for compressing large images,” Inform. Telekomm. Upr. 1(72), 46–51 (2009).
3. A. N. Grigor’ev and E. A. Dudin, “Method of adaptive compression of satellite images of the Earth’s surface,” Izv. Vyssh. Uchebn. Zaved. Priborostr. 58(3), 179–184 (2015).
4. A. Yu. Grishentsev, “Efficient image compression based on differential analysis,” Zh. Radioelektron. (11), 10–11 (2012).
5. I. A. Lezin and A. V. Solov’ev, “Image compression using a multilayer perceptron,” Izv. Samar. Nauch. Tsen. 18(4(4)), 770–773 (2016).
6. E. A. Dudin, S. A. Karin, and A. N. Grigor’ev, “Compression of multispectral remote-Earth-probing data using the principal-component method,” Inf. Kosmos (4), 77–81 (2014).
7. A. N. Grigor’ev and E. A. Dudin, “Procedure for restructuring gray-scale images formed by optoelectronic methods for remote probing of the Earth,” J. Opt. Technol. 84(4), 233–236 (2017) [Opt. Zh. 84(4), 20–24 (2017)].
8. H. H. Harman, Modern Factorial Analysis (U. Chicago Press, Chicago, 1967; Statistika, Moscow, 1972).
9. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, Cambridge, Mass., 2009; Vil’yams, Moscow, 2006).