Generalised Bayesian cloud detection for satellite imagery. Part 1: Technique and validation for night-time imagery over land and sea

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

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

17 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, this is done over both land and ocean using night-time (infrared) imagery. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 87% and 48% for ocean and land, respectively using the Bayesian technique, compared to 74% and 39%, respectively for the threshold-based techniques associated with the validation dataset.
Translated title of the contributionGeneralised Bayesian cloud detection for satellite imagery. Part 1: Technique and validation for night-time imagery over land and sea
Original languageEnglish
Pages (from-to)2573 - 2594
JournalInternational Journal of Remote Sensing
Volume31
DOIs
Publication statusPublished - Mar 2010

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