Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 22, 2025
Number 1 Number 2 Number 3
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 3, pp. 33-52

Verification of georeferencing quality of data and information products of Russian remote sensing space systems

A.I. Vasilyev 1 , S.M. Sokolov 2 
1 Research Center for Earth Operative Monitoring, Moscow, Russia
2 Keldysh Institute of Applied Mathematics RAS, Moscow, Russia
Accepted: 18.03.2025
DOI: 10.21046/2070-7401-2025-22-3-33-52
The article considers the issues of georeferencing quality control for data and information products of Earth remote sensing. A methodological apparatus is proposed for monitoring satellite images of different photogrammetric processing levels using the approach based on comparison of such an image with a known well-georeferenced image for quality assessment. Algorithmic features are presented for assessing the georeferencing quality of archive data of the Operator of Space Systems for Earth Remote Sensing that contains mainly data not transformed into a cartographic projection. The results of the assessments are demonstrated for the images of the Kanopus-V and Resurs-P remote sensing space systems. For the Kanopus-V data (sensor resolution 2.1 m), the CE90 (Circular Error) criterion corresponds to a threshold of no more than 67 m. For the Resurs-P data (sensor resolution 0.7 m), the CE90 criterion corresponds to a threshold of no more than 30.8 m. The obtained assessments demonstrate an accuracy of the Operator’s archive data better than the results provided by other researchers. The reasons for these differences are substantiated. In addition, the article considers quality assessment of mosaic coverings to verify the georeferencing and control the bands synthesis. For the output orthoproducts of Kanopus-V, formed in the Operator’s technological process, the evaluation results are demonstrated: a threshold value of no more than 8.4 m corresponds to the CE90 criterion. The conclusion notes the relevance of the solutions proposed in the article in view of creation of prospective multi-satellite constellations.
Keywords: Earth remote sensing, spacecraft, data processing, information product, quality control, georeferencing, Kanopus-V, Resurs-P, mosaic coverage, seamless continuous coverage
Full text

References:

  1. Alexanin A. I., Krasnopeev S. M., Morozov M. A., Fomin E. V., Superimposition of images from Russian Resource-P satellites, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, V. 15, No. 1, pp. 18–28 (in Russian), DOI: 10.21046/2070-7401-2018-15-1-18-28.
  2. Alexanin A. I., Morozov M. A., Fomin E. V., Automatic update of seamless coverage based on Kanopus-V satellite data, Materialy 18-i Vserossiiskoi otkrytoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 18th All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAS, 2020, p. 425 (in Russian), DOI: 10.21046/18DZZconf-2020a.
  3. Baklanov A. I., Afonin A. N., Blinov V. D., Zabiyakin A. S., CWSME — complex of wide-swath multispectral equipment for the “Resource-P” spacecraft, Vestnik of the Samara University. Aerospace and Mechanical Engineering, 2016, V. 15, No. 2, pp. 22–29 (in Russian), DOI: 10.18287/2412-7329-2016-15-2-22-29.
  4. Vasilyev A. I., Calibration of Kanopus-V satellite sensor during its operation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, V. 12, No. 1, pp. 203–214 (in Russian).
  5. Vasilyev A. I., Development of the automatic control methods for the georeferencing data accuracy of the Meteor-M No. 2 Spacecraft Multispectral Imaging System, Raketno-kosmicheskoe priborostroenie i informatsionnye sistemy, 2023, V. 10, Iss. 1, pp. 31–41 (in Russian), DOI: 10.30894/issn2409-0239.2022.10.1.31.41.
  6. Vasilyev A. I., Stremov A. S., Kovalenko V. P., Study of Resurs-P wide-swath multispectral equipment data applicability to spectrometric tasks, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, V. 14, No. 4, pp. 36–51 (in Russian), DOI: 10.21046/2070-7401-2017-14-4-36-51.
  7. Vasiliev A. I., Krylov A. V., Pankin A. V., Standard level processing of Resurs-P KShMSA data for automatic generation of seamless continuous coverage, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, V. 16, No. 2, pp. 18–28 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-18-28.
  8. Vasiliev A. I., Krylov A. V., Alexeevskiy A. S. et al. (2020a), Stream processing of data from the Kanopus-V satellite constellation to ensure the formation of basic remote sensing products, Materialy 18-i Vserossiiskoi otkrytoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 18th All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAS, 2020, p. 431 (in Russian), DOI: 10.21046/18DZZconf-2020a.
  9. Vasil’ev A. I., Mikheev A. A., Evlashkin M. A., Ol’shevskii N. A. (2020b), Technologies for the formation and provision of global reference coverage of the Earth based on Landsat data, Materialy 18-i Vserossiiskoi otkrytoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 18th All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAS, 2020, p. 432 (in Russian), DOI: 10.21046/18DZZconf-2020a.
  10. Vasilyev A. I., Stremov A. S., Kovalenko V. P. et al. (2023a), Absolute radiometric calibration and validation of Aist-2D satellite data based on Moon imagery, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, V. 20, No. 4, pp. 30–41 (in Russian), DOI: 10.21046/2070-7401-2023-20-4-30-41.
  11. Vasil’ev A. I., Krylov A. V., Mikheev A. A. et al. (2023b), Technology for creating seamless continuous coverage of high spatial resolution on the territory of Russia based on data the Kanopus-V satellite constellation, Materialy 21-i Mezhdunarodnoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 21st Intern. Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAS, 2023, p. 71 (in Russian), DOI: 10.21046/21DZZconf-2023a.
  12. GOST R 59480-2021. Dannye distantsionnogo zondirovaniya Zemli iz kosmosa. Urovni obrabotki dannykh distantsionnogo zondirovaniya Zemli iz kosmosa (GOST R 59480-2021. Remote Sensing Data from Space. Levels of Processing Remote Sensing Data from Space), Moscow: Standartinform, 2021, 16 p. (in Russian).
  13. Eremeev V. V., Zinina I. I., Kuznetsov A. E. et al., Stream processing technology of high-resolution remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, V. 18, No. 1, pp. 11–18 (in Russian), DOI: 10.21046/2070-7401-2021-18-1-11-18.
  14. Zhukov B. S., Kondrat’eva T. V., Polyansky I. V., Permitina L. I., In-flight radiometric cross-calibration of Multispectral Satellite Imaging System on-board Meteor-M No. 1 relative spectroradiometer MODIS on-board Terra, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, V. 11, No. 2, pp. 123–137 (in Russian).
  15. Kuznetsov A. E., Pipeline technologies for streaming data processing from high resolution remote sensing systems, 4-ya Mezhdunarodnaya nauchno-tekhnicheskaya konferentsiya “Aktual’nye problemy sozdaniya kosmicheskikh sistem distantsionnogo zondirovaniya Zemli” (4th Intern. Scientific and Technical Conf. “Actual problems of creating space systems for Earth remote sensing”), Book of abstr., Moscow, 2016, pp. 148–150.
  16. Kuznetsov A. E., Poshehonov V. I., Ryzhikov A. S., Automatic georeference accuracy control technology based on reference pictures from the observation satellite Landsat-8, Digital Signal Processing, 2015, No. 3, pp. 37–42 (in Russian).
  17. Loupian E. A., Savorskiy V. P., Basic products of Earth Remote Sensing Data Processing, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, V. 9, No. 2, pp. 87–96 (in Russian).
  18. Nekrasov V. V., Technology for processing images from the Kanopus-V and BKA satellites for cartographic purposes, Sbornik statei po itogam Torzhestvennogo zasedaniya posvyashchennogo 200-letiyu Rossiiskoi voennoi topograficheskoi sluzhby (Proc. of the results of the Ceremonial meeting dedicated to the 200th anniversary of the Russian Military Topographic Service), Moscow: Izd. MIIGAiK, 2012, pp. 87–91 (in Russian).
  19. Plotnikov D. E., Kolbudaev P. A., Zhukov B. S. et al., The collection of multispectral KMSS-M (Meteor-M No. 2) satellite data aimed at quantitative assessment of the Earth surface, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, V. 17, No. 7, pp. 276–282 (in Russian), DOI: 10.21046/2070-7401-2020-17-7-276-282.
  20. Poshekhonov V. I., Kuznecov A. E., Egin M. M., Estimation of approximation accuracy for strict model of space imaging by rational polynomials, Vestnik of Ryazan State Radio Engineering University, 2023, No. 83, pp. 95–101 (in Russian), DOI: 10.21667/1995-4565-2023-83-95-101.
  21. Fedotkin D. I., Borovenskiy E. N., Sysenko D. V. et al., Automatic processing of space imagery in the ground segment of the domestic multi-satellite constellation of remote sensing spacecrafts, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 3, pp. 9–30 (in Russian), DOI: 10.21046/2070-7401-2024-21-3-9-30.
  22. Barath D., Matas J., Noskova J., MAGSAC: Marginalizing Sample Consensus, Proc. IEEE/CVF Conf. “Computer Vision and Pattern Recognition” (CVPR), 2019, pp. 10189–10197, DOI: 10.1109/CVPR.2019.01044.
  23. Bay H., Tuytelaars T., Van Gool L., SURF: Speeded Up Robust Features, In: Computer Vision — ECCV 2006. ECCV 2006, Lecture Notes in Computer Science, Leonardis A., Bischof H., Pinz A. (eds.), V. 3951, Berlin; Heidelberg: Springer, 2006, pp. 404–417, https://doi.org/10.1007/11744023_32.
  24. Dusmanu M., Rocco I., Pajdla T. et al., D2-Net: A trainable CNN for joint detection and description of local features, 2019 IEEE/CVF Conf. “Computer Vision and Pattern Recognition” (CVPR), 2019, P. 8084–8093, DOI: 10.1109/CVPR.2019.00828.
  25. Fischler M. A., Bolles R. C., Random Sample Consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 1981, V. 24, Iss. 6, pp. 381–395.
  26. Grodecki J., IKONOS stereo feature extraction — RPC approach, Proc. 2001 ASPRS Annual Conf. “Gateway to the New Millennium”, 2001, 7 p.
  27. Harris C., Stephens M., A combined corner and edge detector, Alvey Vision Conf., 1988, pp. 147–151, http://dx.doi.org/10.5244/c.2.23.
  28. Huang Q., Guo X., Wang Y. et al., A survey of feature matching methods, IET Image Processing, 2024, V. 18, Iss. 6, pp. 1385–1410, DOI: 10.1049/ipr2.13032.
  29. Jiang X., Ma J., Xiao G. et al., A review of multimodal image matching: Methods and applications, Information Fusion, 2021, V. 73, pp. 22–71, https://doi.org/10.1016/j.inffus.2021.02.012.
  30. Lowe D. G., Distinctive image features from scale-invariant keypoints, Intern. J. Computer Vision, 2004, V. 60, pp. 91–110.
  31. Markov A. N., Vasilyev A. I., Olshevskiy N. A. et al., Geoinformation service of the Russian EO-space systems information products, Proc. 2019 Big Data from Space (BiDS’2019), EUR 29660 EN, Publications Office of the European Union, 2019, pp. 281–284, DOI: 10.2760/848593.
  32. Scheffler D., Hollstein A., Diedrich H. et al., AROSICS: An automated and robust open-source image co-registration software for multi-sensor satellite data, Remote Sensing, 2017, V. 9, Iss. 7, Article 676, 21 p., https://doi.org/10.3390/rs9070676.