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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
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