ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 853–860, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-853-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 853–860, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-853-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Aug 2020

03 Aug 2020

CLOUD DETECTION FOR NIGHT-TIME PANCHROMATIC VISIBLE AND NEAR-INFRARED SATELLITE IMAGERY

L. Joachim1 and T. Storch2 L. Joachim and T. Storch
  • 1Institute for Photogrammetry (IFP), University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany
  • 2Earth Observation Center (EOC), German Aerospace Center (DLR), Münchener Str. 20, 82234 Weßling, Germany

Keywords: Cloud Detection, Day-Night-Band, Moon Illumination, Night-Time Satellite Imagery, Random Forest, Urban Areas

Abstract. Cloud detection for night-time panchromatic visible and near-infrared (VNIR) satellite imagery is typically performed based on synchronized observations in the thermal infrared (TIR). To be independent of TIR and to improve existing algorithms, we realize and analyze cloud detection based on VNIR only, here NPP/VIIRS/DNB observations. Using Random Forest for classifying cloud vs. clear and focusing on urban areas, we illustrate the importance of features describing a) the scattering by clouds especially over urban areas with their inhomogeneous light emissions and b) the normalized differences between Earth’s surface and cloud albedo especially in presence of Moon illumination. The analyses substantiate the influences of a) the training site and scene selections and b) the consideration of single scene or multi-temporal scene features on the results for the test sites. As test sites, diverse urban areas and the challenging land covers ocean, desert, and snow are considered. Accuracies of up to 85% are achieved for urban test sites.