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Proceedings Paper

Performance of the interactive procedures for daytime detection of dense clouds in the MTI pipeline
Author(s): Charles A. Rohde; Karen Lewis Hirsch; Anthony B. Davis
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Paper Abstract

Pixel-scale cloud detection relies on the simple fact that dense-enough clouds are generally brighter, whiter, and colder than the underlying surface. These plain-language statements are readily translated into threshold operations in the multispectral subspaces, thus providing a reasonable premise for searching data cubes for cloud signatures. To supplement this spectral input (VIS, NIR, and TIR channels), we remark that cloud tops are generally above most of the water vapor in the atmosphere column. An extra threshold in the MTI water vapor product can therefore be applied. This helps considerably in cases where one of the default cloud signatures becomes ambiguous. The resulting cloud mask is however still highly sensitive to the thresholds in brightness, whiteness, temperature, and column water content, especially since we also want to flag low-level clouds that are not-so-dense. Clouds are also generally spatially large. This implies that simple spatial morphological filters can be of use to remove false positives and for expansion of the cloud mask. A false positive is indeed preferable to a miss in the view of MTI's mission in support of nuclear non-proliferation; non-local cloud radiative effects can otherwise bias retrievals in adjacent cloud-free areas. Therefore we use a data analyst to ensure built in quality control for MTI cloud masks. When looking for low-level clouds, the analyst interacts with a GUI containing histograms, a customized RGB rendering of the input data, and an RGB diagnostic cloud mask for quick evaluation of all threshold values. We use MTI data to document the performance and analyst-sensitivity to this procedure.

Paper Details

Date Published: 20 August 2001
PDF: 9 pages
Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); doi: 10.1117/12.437010
Show Author Affiliations
Charles A. Rohde, Los Alamos National Lab. (United States)
Karen Lewis Hirsch, Los Alamos National Lab. (United States)
Anthony B. Davis, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 4381:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII
Sylvia S. Shen; Michael R. Descour, Editor(s)

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