Generalised Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for day-time imagery

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

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)

Abstract

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.
Translated title of the contributionGeneralised Bayesian cloud detection for satellite imagery. Part 2: Technique and validation for day-time imagery
Original languageEnglish
Pages (from-to)2595 - 2621
JournalInternational Journal of Remote Sensing
Volume31
DOIs
Publication statusPublished - Mar 2010

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