Integrating remote sensing data and ground information for solving natural resource and environmental problems

1Khyzhniak, AV, 1Fedorovskyi, OD
1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
Space Sci. & Technol. 2020, 25 ;(4):31-37
https://doi.org/10.15407/knit2020.04.031
Publication Language: English
Abstract: 
We consider the rationale for the integration of remote sensing data and ground information using a statistical criterion to solve natural resources and environmental problems. The method is proposed to be implemented as the module of the “computer assistant”. The algorithm that describes a set of the sequence of operations for automation of the decision-making procedure is presented. This algorithm should free the operator from a significant amount of subjective and labor-intensive work, which is performed using visual methods.
          Based on the proposed methodology, we perform the object recognition in aerospace images of the territory with different geological and landscape conditions and, respectively, with standard objects of different classes with different sets of values of informative features (of different nature and dimension). For the recognition and classification of images of studied objects, the probabilities of the ratios of informative features of the studied areas to ones of each standard object present in the aerospace image were determined.
           The results of testing the proposed methodology are presented on the examples of assessing oil and gas prospects in the areas of the Dnieper-Donetsk cavity and the problem of classifying crops of different varieties in different periods of vegetation in the agricultural fields in the Kyiv region.
Keywords: integration, natural resource, remote sensing, statistical criterion, system analysis
References: 
1. Arkhipov A. I., Glazunov N. M., Khyzhniak A. V. (2018). Heuristic criterion for class recognition by spectral brightness. Cybernetics and Systems Analysis, 54(1), 105—110.
2. Kemker R., Salvaggio C., Kanan C. (2018). Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS J. Photogramm. Remote Sens, 145, 60—77.
3. Knopov P. S., Kasitskaya E. J. (2002). Empirical estimates in stochastic optimization and identification. New York: Kluwer Acad. Publ.
4. Kolmogorov A. N. (2005). Selected Works. Vol. 2. Probability theory and mathematical statistics. Moscow: Mat. Institute named after V. A. Steklova RAS. Science, 584 р. [in Russian].
5. Lyalko V. I., Fedorovsky O. D., Popov M. O., et al. (2004). Using satellite data to study natural resource issues. Space Research in Ukraine. 2002—2004. Kyiv, 7—14 [in Russian].
6. Popov M. A., Stankevich S. A., Arkhipov A. I., Titarenko O. V. (2018). About possibility of hydrocarbon deposit remote detection using computer assistance. Ukrainian J. Remote Sensing, 16, 34—40.
URL: https://www.ujrs.org.ua/ujrs/article/view/119 (Last accessed 08.08.2019).
7. Popov M. A., Stankevich S. A., Topolnytskyj M. V., Titarenko O. V. (2017). Integration of remote and geological-geophysical data in the search for oil and gas deposits on land. Ecological safety and nature management, Vol. 1-2 (23), 36—43 [in Ukrainian].
8. Ren S., He K., Girshick R., Sun J. (2016). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137—1149.
9. Vapnik V. N. (1998). Statistical learning theory. New York: Wiley.
10. Webb A. R., Copsey K. D. (2011). Statistical Pattern Recognition (3rd ed.). New York: John Wiley.
11. Yakimchuk V. G., Sukhanov K. Yu. (2018). Estimation of hydrophysical characteristics of the aquatic environment using satellite images in the context of incomplete information, 19, 33—38.
URL: http://ujrs.org.ua/ujrs/ (Last accessed 08.08.2019).