Method of automated correction of instrument distortions on Landsat-7 multispectral satellite images

1Bilousov, KG, 2Nechyporuk, МV, 1Khoroshylov, VS, Svynarenko, DM, 3Mozgovoy, DK, 1Popel', VM
1Yangel Yuzhnoye State Design Office, Dnipro, Ukraine
2National Aerospace University «Kharkiv Aviation Institute», Kharkiv, Ukraine
3Oles Honchar Dnipro National University, Dnipro, Ukraine
Space Sci. & Technol. 2022, 28 ;(3):17-28
https://doi.org/10.15407/knit2022.03.017
Publication Language: Ukrainian
Abstract: 
An alternative method of automated correction of instrumental distortions of the ETM + scanner on Landsat-7 multispectral satellite images is proposed. The method is based on the application of filtering in the field of spatial frequencies using fast Fourier transform and spectral masks. The parameters of the mask are determined by a known spatial period of interference or experimentally by the results of the filtration. To filter the area of spatial frequencies to be removed, a filtering mask is applied, consisting of pixels with zero values. Periodic components whose spatial period corresponds to the spatial frequencies filtered on the Fourier image will be removed from the filtered image. The quality of filtration is determined by the mask used. When using multispectral images, a filter mask based on the Fourier image of one of the spectral channels is applied to filter other channels.
       The results of visual analysis of the processed images showed a fairly high-quality correction and elimination of instrumental distortions of the ETM + scanner in comparison with other methods. The main advantages of the proposed method are the next: the ability to work without the use of metadata and masks of instrumental distortion; work with single-channel and multispectral images; ability to work with small fragments of the scene; minimum number of manual settings for processing procedures; possibility to work without additional pictures for other dates; high stability of the used algorithms when using images from different satellites; fairly good repeatability of the results on satellite images taken in different seasons and for different areas.
      Experimental testing of the proposed method on a large number of images also confirmed the good repeatability and high stability of the algorithms used. It is expected that the developed technology will also be successfully used to correct instrumental spatial-periodic distortions in archival satellite images obtained using other optical-mechanical scanners.
Keywords: ETM + scanner, fast Fourier transform, instrumental distortions, Landsat-7 satellite, multispectral images, spectral masks
References: 
1. Artyushenko M. V., Tomchenko O. V. (2020). Percolation model to control the distribution of forest infections on images from space vehicles. Space Sci. & Technol., 26, № 4 (125), 45—56.
https://doi.org/10.15407/knit2020.04.045
2. Brejsuell R. (1990). Hartley transform. Theory and applications. M.: Mir (in Russian)
3. Zlobin S. L, Stal’noj A. Ya. (2004). Two-dimensional Fast Hartley Conversion in digital image processing. Proc. of A.S. Popov RSTSREEC, 2, 114—116 (in Russian).
4. Makarov O. L., Bіlousov K. G., Svinarenko D. N., Khoroshylov V. S., Mozgovoy D. K., Popel V. M. (2021). Automatized recognition of urban vegetation and water bodies by Jilin-1А satellite images. Space Sci. & Technol., 27, № 4, 42—53.
https://doi.org/10.15407/knit2021.04.042
5. Makarov A. L., Mozgovoj D. K., Horoshilov V. S., Balashov V. N., Maslyey D. V., Popel’ V. M. (2014). Efficient filtering of space-periodic distortions on archive images. Kosm. nauka tehnol., 20, № 4, 14—21.
https://doi.org/10.15407/knit2014.04.014.
6. Makarov A. L., Mozgovoj D. K., Khoroshilov V. S., et al. (2014). Effective Method Filtration of Attachments on Snacks from Opto-Mechanical Scanners. The V Int. Forum “Applied radioelectronics. Share and prospects for development”, 14–17 October 2014., Kharkiv, KNURE. Vol. 1. Conference “Integrated information radio-electronic system and technologies”, 105—108.
7. Maslej V. N., Mozgovoj D. K., Bilousov K. G., Horoshilov V. S., Bushanska O. S., Galich N. G. (2016). Methods of the impact evaluation of amber mining by multispectral satellite images. Space Sci. & Technol., 22, № 6, 26—36.
https://doi.org/10.15407/knit2016.06.026
8. Mozgovoj D. K. (2008). Consigned combined masks for filtration of periodic interference. Interagency scientific and technical collection “Applied geometry and engineering graphics”. K.: Ukrainian Association of Applied Geometry, 175—179.
9. Mozgovij D. K. Voloshin V. І. (2003). Filtration of spatial-periodic devices for satellite images. Publ. Taurian State Agrotechnical Academy. № 4. Applied geometry and ingineering graphics, 20, 71–75.
10. Mozgovoj D. K., Voloshin V. I., Bushuev E. I. (2004). Filtration of radiometric interference with a space-periodic structure. Problems of Control and Informatics, № 3, 97—106.
11. Omelych I., Yaremenko A., Neposhyvailenko N., Ghoraj I. (2019). Determination of vegetation cover trends based on the calculation of the normalized vegetation index on the example of Petrykivskyi district of Dnipropetrovsk region. Ukrainian J. Remote Sensing, № 23, 9—13.
https://doi.org/10.36023/ujrs.2019.23.159
12. Popov M. O., Lyal’ko V. I., Stankevich S. A. (2019). Ukrainian national system for Earth’s remote sensing: look for efficient solutions. Space Sci. & Technol., 25, № 6, 39—50.
https://doi.org/10.15407/knit2019.06.039
13. Fedorovskyi O. D., Zub L. N., Dyachenko T. N., Tomchenko O. V., Khyzhniak A. V., Yakymchuk V. H. (2020). Remote assessment of the ecological state of water bodies based on the multidimensional density distribution of biotope areas on the example of the Kyiv reservoir. Space Sci. & Technol., 26, № 5 (126), 38—47.
https://doi.org/10.15407/knit2020.05.038
14. Shelestov A. Yu., Yailymov B. Ya., Yailymova H. O., Bilokonska Y. V., Nivievskyi O. V. (2020). Satellite crop monitoring for Ukraine. Space Sci. & Technol., 26, № 6 (127), 27—37. https://doi.org/10.15407/knit2020.06.027
https://doi.org/10.15407/knit2020.06.027
15. Yailymov B. Ya., Lavreniuk M. S., Shelestov A. Yu., Kolotіi A. V., Yajlymova G. O., Fedorov O. P. (2018). Methods of essential variables determination for the Earth’s surface state assessing. Space Sci. & Technol., 24, № 4, 24—37.
https://doi.org/10.15407/knit2018.04.026
16. DestripeLandsat-7 ETM+. URL: https://blamannen.wordpress.com/2011/07/12/ etm-some-thoughts (Last accessed: 30.11.2021).
17. Filling the Gaps to use in Scientific Analysis. URL: http://landsat.usgs.gov/sci_an.php (Last accessed: 30.11.2021).
18. Gap Fill for Landsat 7 images — A correction of SLC-off / Luis Vega Bustillos, Environmental Engineer DATE: July 2012.
19. Hnatushenko V. V., Mozgovoy D. K., Spirintsev V. V., Udovyk I. M. (2019). All-weather monitoring of oil and gas production areas using satellite data. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, № 6, 137—143.
https://doi.org/10.29202/nvngu/2019-6/20
20. Hnatushenko V. V., Mozgovoy D. K., Vasyliev V. V., Kavats O. O. (2017). Satellite Monitoring of Consequences of Illegal Extraction of Amber in Ukraine. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, № 2, 99—105.
21. Hnatushenko V. V., Mozgovoy D. K., Vasyliev V. V. (2017). Satellite monitoring of deforestation as a result of mining. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, № 5, 94—99.
22. How to fill gaps in Landsat ETM images. URL: https://yceo.yale.edu/ landsat-etm-images (Last accessed: 30.11.2021).
23. Landsat 7 ETM+ Satellite Sensor. URL: https://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors... (Last accessed: 30.11.2021).
24. Landsat 7 Data Users Handbook / Department of the Interior U.S. Geological Survey, Version 2.0. EROS, Sioux Falls, South Dakota, 2019.
25. Landsat SLC-off: propushchenі smugi ne vіdnoviti? URL: https:// gis-lab.info/forum/viewtopic.php?t=4357 (Last accessed: 30.11.2021).
26. Landsat 7 Satellite SLC Gap Fill Methodology. URL: https://landsat.usgs.gov/sites/default/ files/documents/SLC_Gap_Fill_Methodology.pdf (Last accessed: 30.11.2021).
27. Mozgovoy D., Tsarev R., Svinarenko D., Danichev A., Karnaukhov A. (2020). Instrumental Distortion Correction Method for the ETM + Scanner on Landsat-7 Multispectral Satellite Images. Int. J. Engineering Research and Technology, 13(12), 4799—4803.
28. Mozgovoy D. K., Voloshin V. I., Bushuev E. I. Filtration of Radiometric Interference with a Space-Periodic Structure. J. Automation and Inform. Sci., 36. i6.20, 14—22.
https://doi.org/10.1615/JAutomatInfScien.v36.i6.20
29. Removing stripes from Landsat-7 SLC OFF images. URL: https://community.esri.com/ thread/164902 (Last accessed: 30.11.2021).
30. Satellite Missions Database. URL: https://directory.eoportal.org/web/eoportal/satellite-missions/l/landsat-7 (Last accessed: 30.11.2021).,
31. SLC-off Products: Background. Obtenido de USGS — Landsat Missions. URL: http://landsat.usgs. gov/using_Landsat_7_data.php (Last accessed: 30.11.2021).