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Bayesian Cloud Detection for Meris, Aatsr, and Their Combination : Volume 7, Issue 11 (06/11/2014)

By Hollstein, A.

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Book Id: WPLBN0004000054
Format Type: PDF Article :
File Size: Pages 41
Reproduction Date: 2015

Title: Bayesian Cloud Detection for Meris, Aatsr, and Their Combination : Volume 7, Issue 11 (06/11/2014)  
Author: Hollstein, A.
Volume: Vol. 7, Issue 11
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Henken, C. C., Preusker, R., Fischer, J., & Hollstein, A. (2014). Bayesian Cloud Detection for Meris, Aatsr, and Their Combination : Volume 7, Issue 11 (06/11/2014). Retrieved from

Description: Institute for Space Sciences, Department of Earth Sciences, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany. A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud masks were designed to be numerically efficient and suited for the processing of large amounts of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient amounts of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Bayesian cloud detection for MERIS, AATSR, and their combination

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