Generalized Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for daytime imagery

S. Mackie, C. J. Merchant, O. Embury, P. Francis

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.

Original languageEnglish
Pages (from-to)2595-2621
Number of pages27
JournalInternational Journal of Remote Sensing
Volume31
Issue number10
DOIs
Publication statusPublished - 2010

Keywords / Materials (for Non-textual outputs)

  • NUMERICAL WEATHER PREDICTION
  • SEA-SURFACE TEMPERATURE
  • BIDIRECTIONAL REFLECTANCE
  • DATA ASSIMILATION
  • INFRARED IMAGERY
  • RETRIEVAL
  • ALGORITHM
  • CLASSIFICATION
  • RADIOMETER
  • WHITECAPS

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