Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3
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, 2022, Vol. 19, No. 5, pp. 53-62

Using NDSI index to distinguish clouds and non-snow surfaces in multispectral images

О.V. Nikolaeva 1 
1 Keldysh Institute of Applied Mathematics RAS, Moscow, Russia
Accepted: 26.10.2022
DOI: 10.21046/2070-7401-2022-19-5-53-62
Using the NDSI (Normalized Difference Snow Index) to distinguish clouds from desert areas and urbanized ones in multi-spectral images of the Hyperion spectrometer is under consideration. Hyperion images taken over surfaces of various types (water, desert, green vegetation, urbanized areas) in both clear sky and overcast conditions were selected. Statistical analysis of NDSI index value samples was performed. The same analysis of the DSI (Desert Sand Index) value samples being used in the standard cloud mask Hyperion algorithm was performed. Thresholds to distinguish clouds from the surface of each type are obtained. The percentages of misclassified pixels are estimated. It is shown that the NDSI index with the obtained thresholds can be used to reliably distinguish clouds from desert areas and urbanized areas in Hyperion images. The DSI index is shown to distinguish clouds from desert areas by larger errors, and cannot be used to distinguish clouds from urbanized areas. Hyperion images with broken clouds are considered. Bar chart analysis is shown to give optimal thresholds for an image. It is shown that the separation of pixels into “lighted surface” and “cloud and cloud shadow” is performed via the NDSI index with greater accuracy than via the DSI index. Further separation of cloudy pixels and shadow pixels should be performed via other spectral indexes. The presented results are proposed to be used in algorithms of detecting clouds in images of spectrometers with no radiation temperature measurements.
Keywords: cloud detection, multispectral images, NDSI, DSI
Full text

References:

  1. Amikishieva R. A., Raputa V. F., Yaroslavtseva T. V., Analysis technologies of atmospheric pollution processes based on ground and satellite observations, InterExpo, Geo-Sibaria, 2020, Vol. 4, No. 1, pp. 36–41 (in Russian), DOI: 10.33764/2618-981X-2020-4-1-36-41.
  2. Belova E. I., Ershov D. C., Preprocessing LandSat TM-/ETM+ Data Sets for Creating Cloud-Free Spring, Autumn and Winter Imagery, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 4, pp. 9–14 (in Russian).
  3. Volkova E. V., Automatic estimation of cloud cover and precipitation parameters obtained by AVHRR NOAA for day and night conditions, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 5, pp. 300–320 (in Russian), DOI: 10.21046/2070-7401-2017-14-5-300-320.
  4. Krutskikh N. V., Kravchenko I. Yu., The use of Landsat satellite images for geoecological monitoring of urbanized areas, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 2, pp. 159–168 (in Russian), DOI: 10.21046/2070-7401-2018-15-2-159-168.
  5. Orlov Yu. N., Fedorov S. L., Methods of numerical modeling of transient random wandering processes, Moscow: MIPT, 2016, 108 p. (in Russian).
  6. 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: Univ. Wisconsin, 2010, 121 p.
  7. Barton J. S., Casey K., Chien J. Y. L., Digirolamo N. E., Klein A. G., Powell H. W., Tait A. B., Hall D. K., Riggs G. A., Solomonson V. V., Algorithm Theoretical Basis Document for the MODIS Snow and Sea Ice-Mapping Algorithms, Madison: Univ. Wisconsin, 2001, 45 p.
  8. Frantz D., Haß E., Uhl A., Stoffels J., Hill J., Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects, Remote Sensing of Environment, 2018, Vol. 215, pp. 471–481, DOI: 10.1016/j.rse.2018.04.046.
  9. Gómez-Chova L., Camps-Valls G., Calpe-Maravilla J., Guanter L., Moreno J., Cloud-Screening Algorithm for ENVISAT/MERIS Multispectral Images, IEEE Trans. Geoscience and Remote Sensing, 2007, Vol. 45, No. 12, pp. 4105–4118, DOI: 10.1109/TGRS.2007.905312.
  10. Griffin M., Burke H., Mandle D., Miller J., Cloud cover detection algorithm for EO-1 Hyperion imagery, Proc. IEEE Intern. Geoscience and Remote Sensing Symp., Toulouse, France, 2003, No. 03CH37477, DOI: 10.1109/IGARSS.2003.1293687.
  11. Hagolle O., Huc M., Villa Pascual D., Dedieu G., A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images, Remote Sensing of Environment, 2010, Vol. 114, pp. 1747–1755, DOI: 10.1016/j.rse.2010.03.002.
  12. Irish R. R., Barker J. L., Goward S. N., Arvidson T., Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) algorithm, Photogrammetric Engineering and Remote Sensing, 2006, Vol. 72, No. 10, pp. 1179–1188, DOI: 10.14358/PERS.72.10.1179.
  13. Li Z., Shen H., Weng Q., Zhang Y., Dou P., Zhang L., Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects, ISPRS J. Photogrammetry and Remote Sensing, 2022, Vol. 188, pp. 89–108, DOI: 10.1016/j.isprsjprs.2022.03.020.
  14. Lyapustin A., Wang Y., Frey R., An automatic cloud mask algorithm based on time series of MODIS measurements, J. Geophysical Research, 2008, Vol. 113, D16207, DOI: 10.1029/2007JD009641.
  15. Shendryk Y., Rist Y., Ticehurst C., Thorburn P., Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery, ISPRS J. Photogrammetry and Remote Sensing, 2019, Vol. 157, pp. 124–136, DOI: 10.1016/j.isprsjprs.2019.08.018.
  16. Stournara P., Tsakiri-Strati M., Patias P., Detection and removal of cloud and cloud shadow contamination from hyperspectral images of Hyperion sensor, South-Eastern European J. Earth Observation and Geomatics, 2013, Vol. 2, No. 1, pp. 33–44.
  17. Thompson D. R., Green R. O., Keymeulen D., Lundeen S. K., Mouradi Y., Nunes D., Castano R., Chien S. A., Rapid spectral cloud screening onboard aircraft and spacecraft, IEEE Trans. Geoscience and Remote Sensing, 2014, Vol. 52, No. 11, pp. 6779–6792, DOI: 10.1109/TGRS.2014.2302587.
  18. Zhai H., Zhang H., Zhang L., Li P., Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery, ISPRS J. Photogrammetry and Remote Sensing, 2018, Vol. 114, pp. 235–253, DOI: 10.1016/j.isprsjprs.2018.07.006.
  19. Zhu Z., Woodcock C. E., Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 2012, Vol. 118, pp. 83–94, DOI: 10.1016/j.rse.2011.10.028.