SEARCH METHOD FOR CHANGES OF THE EARTH’S SURFACE STATE THROUGH MULTI-TEMPORAL SATELLITE IMAGES

Altukhov A.I., Korshunov D.S. Search method for changes of the earth’s surface state through multi-temporal satellite images. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 3, pp. 410–416 (in Russian). doi: 10.17586/2226-1494-2019-19-3-410-416

Subject of Research. We study the approach to the underlying surface interpretation automation for satellite images obtained by the onboard optical-electronic equipment of the Earth remote sensing systems. The topicality of research is determined by the necessity of introduction of computer vision methods aimed at solving the search problem of the earth’s surface state changes through multi-temporal satellite monitoring data. The goal of research is reducing of the time spent on processing of large area satellite images. Method. The method is based on the idea of comparing the contrast of different-time satellite images. For method implementation, a mathematical apparatus is formed for calculating the contrast values of the analyzed images in the normalized interval from 0 to 1. The effectiveness of automated processing of satellite images is ensured by their pre- segmentation and zoning. Segmentation parameters are selected taking into account the size of the objects to be detected. The efficiency of the proposed method is confirmed by the high correlation of the automated processing results with the results of visual analysis of test satellite images. Main Results. The results of calculating the contrast of test images using the formulated mathematical apparatus are presented. The necessity of image segmentation is proved to solve the problem of detecting changes in the terrain on the example of processing images consisting of different number of fragments. An approach is developed for reducing the redundancy of data on terrain changes by performing a preliminary zoning procedure. The essence of this procedure is to determine the researched area boundaries in order to limit the zones for search of changes. Practical Relevance. The proposed method of data processing on the Earth remote sensing provides interpretation of the underlying surface images in an automated mode without operator participation. At that, the interpretation of images, when observing large areas, can be accelerated.

1. Veselov Yu.G., Ostrovskii A.S., Sel'vesyuk N.I., Krasavin I.V. Estimation of the limiting resolution of digital optoelectronic systems of remote sensing using the theory of linear systems. Izvestiya YuFU. Tekhnicheskie Nauki, 2013, no. 3, pp. 84–89. (in Russian)
2. Gorbachev A.A., Korotaev V.V., Yaryshev S.N. Solid-State Matrix Photoconverters and Cameras Based on Them. St. Petersburg, NRU ITMO Publ., 2013, 98 p. (in Russian)
3. Korotaev V.V., Maraev A.A. Sources and Detectors of Optical Radiation. St. Petersburg, ITMO University, 2017, 104 p.
4. Grigor'ev A.N., Korshunov D.S., Belyaev A.S. Forecasting quality satellite images of space remote sensing systems. Trudy Voenno-Kosmicheskoi Akademii im. A.F. Mozhaiskogo, 2010, no. 629, pp. 143–147. (in Russian)
5. Baklanov A.I. Observation and Monitoring Systems. Moscow, Binom Publ., 2009, 234 p. (in Russian
6. Altukhov A.I., Korshunov D.S., Shabakov E.I. Method of image quality enhancement for space objects. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2014, no. 4, pp. 35–40. (in Russian)
7. Altuhov A.I., Shabakov E.I., Korshunov D.S. Increased image quality by synthesizing space photos with different exposures. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 1, pp. 24–30 (in Russian). doi: 10.17586/2226-1494-2017-17-1-24-30
8. Vasil'ev A.S., Krasnyashchikh A.V., Korotaev V.V., Lashmanov O.Yu., Lysenko D.Yu., Nenarokomov O.N., Shirokov A.S., Yaryshev S.N. Unmanned aerial vehicle computer system for wildfire detection by image superimposing. Journal of Instrument Engineering, 2012, vol. 55, no. 12, pp. 50–55. (in Russian)
9. Lashmanov O.U., Vasilev A.S., Vasileva A.V., Anisimov A.G., Korotaev V.V. High-precision absolute linear encoder based on a standard calibrated scale. Measurement, 2018, vol. 123, pp. 226–234. doi: 10.1016/j.measurement.2018.03.071
10. Korotaev V.V., Mel'nikov G.S., Mikheev S.V., Samkov V.M., Soldatov Yu.I. Basics of Thermal Imaging. St. Petersburg, NRU ITMO Publ., 2012, 122 p. (in Russian)
11. Grigoriev A.N., Dudin E.A., Korshunov D.S., Oktiabrskii V.V. The conceptual and analytical models of optoelectronic survey with prior exposure metering on board a spacecraft. Current Problems in Remote Sensing of the Earth from Space, 2017, vol. 14, no. 3, pp. 128–138. (in Russian)
12. Zanin K.A. Image quality based selection of parameters of optical-electronic space observation system. Polet. Obshcherossiiskii Nauchno-Tekhnicheskii Zhurnal, 2007, no. 11, pp. 30–37. (in Russian)
13. Krasil'nikov N.N. Digital Processing of 2D and 3D Images.St. Petersburg, BKhV-Peterburg Publ., 2011, 608 p. (in Russian)
14. Vorobel' R.A. Digital Image Processing on the Basis of the Contrast Theory. Dr. Eng. Sci. Dis. L’vov, Ukraine, 1999, 369 p. (in Russian)
15. Vasil'ev A.S., Korotaev V.V., Krasnyashchikh A.V., Lashmanov O.Yu., Nenarokomov O.N. Superposition of thermal and television images in examination of building units and constructions. Journal of Instrument Engineering, 2012, vol. 55, no. 4, pp. 12–16. (in Russian)
16. Vasilev A.S., Korotaev V.V. Research of the fusion methods of the multispectral optoelectronic systems images. Proceedings of SPIE, 2015, vol. 9530, art. 953007. doi: 10.1117/12.2184554
 

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License