An algorithm for segmentation of aerosol inhomogeneities
Filimonov P.A., Belov M.L., Fedotov Yu.V., Ivanov S.E., Gorodnichev V.A.

 

Graduate school of Bauman Moscow State Technical University, Moscow, Russia
Research Institute of Radioelectronics and Laser Technologies of Bauman Moscow State Technical University, Moscow, Russia

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Abstract:
We have developed an algorithm for segmentation of aerosol inhomogeneities in the registered field of lidar signal fluctuations in the atmosphere in the "Range – Time" coordinates that is based on a sliding window of a two-dimensional autocorrelation function. The obtained algorithm allows us to robustly detect aerosol inhomogeneities, which can be used in practical applications to study aerosol fields in the atmosphere and improve the accuracy of estimating the speed and direction of winds. The developed method was applied for obtaining a histogram of aerosol inhomogeneity-size distribution from 355-nm elastic lidar measurements data in the surface layer.

Keywords:
digital image processing, lidar, aerosol inhomogeneities, surface laye.

Citation:
Filimonov PA, Belov ML, Fedotov YuV, Ivanov SE, Gorodnichev VA. An algorithm for segmentation of aerosol inhomogeneities. Computer Optics 2018; 42(6): 1062-1067. DOI: 10.18287/2412-6179-2018-42-6-1062-1067.

References:

  1. Samoilova SV, Balin YuS, Kokhanenko GP, Penner IE. Investigations of the vertical distribution of troposphere aerosol layers based on the data of multifrequency Raman lidar sensing: Part 1. Methods of optical parameter retrieval. Atmospheric and Ocean Optics 2009; 22(3): 302-315. DOI: 10.1134/S1024856009030075.
  2. Yorks JE, McGill MJ, Palm SP, Hlavka DL, Selmer PA, Nowottnick EP, Vaughan MA, Rodie SD, Hart WD. An overview of the CATS level 1 processing algorithms and data products. Geophysical Research Letters 2016; 43(9): 4632-4639. DOI: 10.1002/2016GL068006.
  3. Weitkamp C. Lidar: Range-resolved optical remote sensing of the atmosphere. New York: Springer-Verlag; 2005. ISBN: 978-0-387-40075-4.
  4. Razenkov IA. Aerosol lidar for continuous atmospheric monitoring. Atmospheric and ocean optics 2013; 26(4): 308-319. DOI: 10.1134/S1024856013040118.
  5. Mayor SD, Dérian P, Mauzey CF, Spuler SM, Ponsardin PL, Pruitt JD, Ramsey D, Higdon NS. Comparison of an analog direct detection and a micropulse aerosol lidar at 1.5-μm wavelength for wind field observations – with first results over the ocean. Journal of Applied Remote Sensing 2016; 10(1): 016031. DOI: 10.1117/1.JRS.10.016031.
  6. Belov ML, Ivanov SE, Gorodnichev VA, Strelkov BV. Laser remote method for measuring gusts of atmospheric wind [In Russian]. Herald of the Bauman Moscow State Technical University, Instrument Engineering 2014; 2(95): 40-52.
  7. Kozintcev VI, Belov ML, Orlov VM, Gorodnichev VA, Strelkov BV, Rozhdestvin VN, ed. Laser remote sensing [In Russian]. Moscow: “BMSTU” Publisher; 2010. ISBN: 978-5-7038-3436-7.
  8. GOST 31581-2012. Laser safety. General safety requirements for the development and operation of laser products [In Russian]. Moscow: “Standardinform” Publisher; 2013.
  9. Matvienko GG, Zade GO, Ferdinandov ES, Kolev IN, Avramova RP. Laser remote sensing correlation methods for measurement of wind velocity [In Russian]. Novosibirsk: “Science” Publisher; 1985.
  10. Djigan VI. Adaptive signal filtering: theory and algorithms [In Russian]. Moscow: “Technosphere” Publisher; 2013. ISBN: 978-5-94836-342-4.
  11. Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Upper Saddle River, NJ: Prentice Hall; 2008. ISBN: 978-0-13-168728-8.
  12. Nikolenko S, Kadurin A, Arkhangelskaya E. Deep learning: Immersion in the world of neural networks [In Russian]. Saint-Petersburg: “Piter” Publisher; 2018. ISBN: 978-5-496-02536-2.

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