Model for selecting the Earth observation satellite systems by object recognition probability

The paper provides a model for selecting satellite systems for optical observation of the Earth by the probability of recognizing objects. The model is based on improved rules for selecting satellites by the spatial resolution of onboard imaging system. The model advantage over the known ones is the satellite systems selection not only due to the spatial resolution of image, but also taking into account the predictable contrast of objects and the required recognition level. The proposed model ensures a more correct pre-selection of satellite systems, in doing so reducing the cost of satellite imaging by preventing extra-requirements for spatial resolution of onboard imaging system. Also, the article proposes a method of forming a database of radiometric contrasts of typical objects and backgrounds for their consideration in the model for choosing satellite systems. This method does not depend on the specific types of on-board equipment, as well as on the availability of archival images at the time of planning satellite imagery. References Mashkov, O. A., Ishchenko, D. A., Pekarev, D. V., Mikhalevich, V. E. (2004). Substantiation of the structure of the automated software-algorithmic complex of forecasting and detection of crisis situations based on the results of the analysis of the space situation. Institute of Modeling Problems in Energy, National Academy of Sciences of Ukraine. (in Ukraine). Merchant, J. W., Narumalani, S., Warner, T. A., Nellis, M.D., Foody, G.M. (2009). Integrating remote sensing and geographic information systems. The SAGE Handbook of Remote Sensing. London: SAGE Publications, 257-268. DOI:https://doi.org/10.4135/9780857021052.n18 Popov, M .A., Markov, S. Y., Stankevich, S. A., Bodnya, O. L. (2007). Cataloging system and distributed access to remote sensing data of the Earth: concept, architecture, implementation. Space Science and Technology (in Russian). Kondratov, A. M., Maslenko, O. V. (2018). Remote sensing satellite virtual constellation optimizing with target recognition probability. Central European Researchers Journal, 22-27. Burshtynska, Kh. V., Stankevych, S. A. (2013). Aerospace shooting systems. Lviv Polytechnic Publishing House. Lviv (in Ukraine) Sjaardema, T. A., Smith, C. S., Birch, G. C. (2015). History and Evolution of the Johnson Criteria.Sandia National Laboratories. Albuquerque. DOI:https://doi.org/10.2172/1222446 Ruirong, Y., Xinwei, W., Pengdao, R., Jun, H., Yan, Z. (2016). Target observation performance evaluation method for video surveillance based on Johnson criteria. Infrared and Laser Engineering. Kononov, V. I. (2002). Substantiation of methods for determining the resolution on the ground of aerospace systems with discrete photodetectors. Space Science and Technology. (in Russian) Sviridov, K. D. (2014). On the maximum resolution of aerospace systems for remote sensing of the Earth. Rocket and space instrumentation and information systems. (in Russian) Stankevich, S. A., Shklyar, S. V. (2005). Advanced algorithm for determining the transition function on a digital aerospace image. Uchenye zapiski Tavricheskogo natsionalnogo universiteta im. V.I. Vernadsky. (in Ukraine) Eremeev, V. V., Knyazkov, P. A. (2010). Estimation of the resolving power of space survey materials based on their statistical analysis. Modern problems of remote sensing of the Earth from space. (in Russian) Molchanov, A. S. (2018). Methods for estimating the linear resolution per pixel of military aerophoto systems during flight tests. Geodesy and aerial photography. (in Russian) Yanchevsky, S. L. (2011). Intelligent methods for optimizing satellite imagery planning. Cybernetics and Computing. (in Russian) Hollands, J. G., Terhaar, P., Pavlovic, N. J. (2018). Effects of resolution, range, and image contrast on target acquisition performance. Human Factors.DOI: https://doi.org/10.1177/0018720818760331 Kobernichenko, V. G., Ivanov, O. Y., Zraenko, S. M., Sosnovsky, A. V., Trenikhin, V. A. (2013). Data processing of remote sensing of the Earth: practical aspects. Ekaterinburg: Ural University Press. (in Russian) Stankevich, S. A. (2007). Methodology of statistical space-frequency processing of hyperspectral aerospace images. Geodesy, cartography and aerial photography. (in Ukraine) Kondratov, O. M. (2018). Improved technology for selecting spacecraft according to the resolution of the camera equipment. Kyiv. (in Ukraine) Popov, M. O., Stankevich, S. A., Zelyk, Ya. I., Shklyar, S. V., Semenov, O. V. (2012). Calibration of the spectral sensitivity of the sensor of the multispectral satellite system "Sich-2" by ground-based spectrometric measurements: preliminary results. Space Science and Technology. (in Ukraine)DOI: https://doi.org/10.15407/knit2012.05.059 ASTM E490-00a (2019). Standard Solar Constant and Zero Air Mass Solar Spectral Irradiance Tables.&lt;/em &gt; Philadelphia: ASTM Headquarters. Doxani, G., Vermote, E., Roger, J. C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., Li, F., Louis, J., Mangin, A., Pahlevan, N., Pflug, B., Vanhellemont, Q. (2018). Atmospheric correction inter-comparison exercise. Remote Sensing.DOI: https://doi.org/10.3390/rs10020352 Shepherd, L. N. (2017). Methods and algorithms for pattern recognition in automated decoding of remote sensing data. Moscow. (in Russian). Zhang, H., Wang, P., Zhang, C., Jiang, Z. (2019). A comparable study of CNN-based single image super-resolution for space-based imaging sensors. Sensors.DOI: https://doi.org/10.3390/s19143234 Dong, C., Loy, C. C., Tang, X., Leibe, B., Matas, J., Sebe, N., Welling, M. (2016). Accelerating the super-resolution convolutional neural network. Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer. Li, K., Wan, G., Cheng, G., Meng, L., Han, J. (2020). Object detection in optical remote sensing images: A survey and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing.DOI: https://doi.org/10.1016/j.isprsjprs.2019.11.023 Kwan, C. (2018). Image resolution enhancement for remote sensing applications. Proceedings of the 2-nd International Conference on Vision, Image and Signal Processing (ICVISP 2018). Las Vegas: Association for Computing Machinery.DOI: https://doi.org/10.1145/3271553.3271590