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Generalized Bayesian cloud detection for satellite imagery. part 1: technique and validation for night-time imagery over land and sea

Mackie, S., Embury, O., Old, C., Merchant, C. J. and Francis, P. (2010) Generalized Bayesian cloud detection for satellite imagery. part 1: technique and validation for night-time imagery over land and sea. International Journal of Remote Sensing, 31 (10). pp. 2573-2594. ISSN 0143-1161

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To link to this item DOI: 10.1080/01431160903051703

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:No Reading authors. Back catalogue items
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:33722
Publisher:Taylor & Francis

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