(46-5) 15 * << * >> * Russian * English * Content * All Issues

Technique of detecting cloudy objects in multispectral images
O.V. Nikolaeva 1

Keldysh Institute of Applied Mathamatics RAS, Moscow

 PDF, 1711 kB

DOI: 10.18287/2412-6179-CO-1076

Pages: 808-817.

Full text of article: Russian language.

A multistep algorithm to detect cloudy objects in multispectral images is presented. Clustering spatial pixels by the k-means method and applying spectral criteria of cloudy/clear sky to fragments of obtained clusters are carried out in each step of the algorithm. One cloudy object is found in one step.
     Results of testing the algorithm on images from a sensor HYPERION (199 non-zero spectral bands in a 425 nm – 2400 nm interval under high spatial resolution of 30 m) are given. Images with discontinuous cloud cover above different surfaces (ocean, vegetation, desert, town, snow) are considered.
     An alternative method, in which the same spectral criteria are applied to each pixel, is also used in testing. Cloud masks obtained by both algorithms are compared. Mean spectra of obtained cloudy objects are given. The presented algorithm finds 1-3 cloudy objects corresponding to the brightness distribution in RGB images. Using the alternative algorithm (without preliminary clustering) leads to detection errors on the cloud edges.
     Three quality parameters are offered. The ratio of dispersion of "cloudy" spectra to dispersion of "clear" spectra is found to be most informative. This ratio should be much less than 1 when using a good cloudy mask.

cloud detection, multispectral images, spectral criteria, quality parameters.

Nikolaeva OV. Technique of detecting cloudy objects in multispectral images. Computer Optics 2022; 46(5): 808-817. DOI: 10.18287/2412-6179-CO-1076.


  1. Taylor TE, O'Dell CW, O'Brien DM, Kikuchi N, Yokota T, Nakajima TY, Ishida H, Crisp D, Nakajima T. Comparison of cloud-screening methods applied to GOSAT near-infrared spectra. IEEE Trans Geosci Remote Sens 2012; 50(1): 295-309. DOI: 10.1109/TGRS.2011.2160270.
  2. Richter R. Atmospheric correction satellite data with haze removal including a haze/clear transition region. Comput Geosci 1996; 22(6): 675-681. DOI: 10.1016/0098-3004(96)00010-6.
  3. Ackerman S, Frey R, Strabala K, Liu Y, Gumley L, Baum B, Menzel P. Discriminating clear-sky from cloud with Modis. Algorithm theoretical basis document (MOD35). Madison: University of Wisconsin; 2010.
  4. Hall DK, Riggs GA, Solomonson VA, Barton JS, Casey K, Chien JYL, Digirolamo NE, Klein AG, Powell HW, Tait AB. Algorithm theoretical basis document for the Modis snow and sea ice-mapping algorithms. Madison: University of Wisconsin; 2001.
  5. Irish RR, Barker JL, Goward SN, Arvidson T. Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) algorithm. Photogramm Eng Remote Sensing 2006; 72(10): 1179-1188. DOI: 10.14358/PERS.72.10.1179.
  6. Griggin M, Burke H, Mandle D, Miller J. Cloud cover detection algorithm for EO-1 Hyperion imagery. Proc IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2003; 1: 86-89. DOI: 10.1109/IGARSS.2003.1293687.
  7. Lyapustin A, Wang Y, Frey R. An automatic cloud mask algorithm based on time series of MODIS measurements. J Geophys Res 2008; 113(D16): 207. DOI: 10.1029/2007JD009641.
  8. Volkova EV. Automatic estimation of cloud cover and precipitation parameters obtained by AVHRR NOAA for day and night conditions [In Russian]. Current Problems in Remote Sensing of the Earth from Space 2017; 14(5): 300-320. DOI: 10.21046/2070-7401-2017-14-5-300-320.
  9. Gao BC, Kaufman YJ, Wiscombe W. Removal of thin cirrus path radiances in the 0.4-1.0 μm spectral region using the 1.375-μm strong water vapor absorption channel. Summaries of the Seventh JPL Airborne Earth Science Workshop 1998; 1: 121-130.
  10. Gómez-Chova L, Camps-Valls G, Calpe-Maravilla J, Guanter L, Moreno J. Cloud-screening algorithm for ENVISAT/MERIS multispectral images. IEEE Trans Geosci Remote Sens 2007; 45(12): 4105-4118. DOI: 10.1109/TGRS.2007.905312.
  11. Thompson DR, Green RO, Keymeulen D, Lundeen SK, Mouradi Y, Nunes D, Castano R, Chien SA. Rapid spectral cloud screening onboard aircraft and spacecraft. IEEE Trans Geosci Remote Sens 2014; 52(11): 6779-6792. DOI: 10.1109/TGRS.2014.2302587.
  12. Korolev EE, Kochergin AM, Kuznetsov AE, Pobaruev VI. Automatic segmentation of cloud objects on the high spatial resolution surface image [In Russian]. Modern Problems of Science and Education 2014; 5: 32-36.
  13. Trigo IF, Freitas SC, Barroso C, Macedi J. Gio global land component – Lot I "Operation of the global land component". Algorithm Theoretical Basis Document for cloud mask for LST retrieval. Copernicus; 2014.
  14. Belov AM, Denisova AY. Scene distortion detection algorithm using multitemporal remote sensing images. Computer Optics 2019; 43(5): 869-885. DOI: 10.18287/2412-6179-2019-43-5-869-885.
  15. Vetrov AA, Kuznetsov AE. Segmentation of cloud objects on panchromatic photos of earth surface [In Russian]. Digit Signal Process 2011; 3: 32-36.
  16. Sobolev VV. Light scattering in planetary atmospheres. New York: Pergamon Press; 1975.
  17. Martins JV, Tanre D, Remer L, Kaufman Y, Mattoo S, Levy R. Modis cloud screening for remote sensing of aerosols over oceans using spatial variability. Geophys Res Lett 2002; 29(12): 1619-1622. DOI: 10.1029/2001GL013252.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20