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Proceedings Paper

Autoadaptive monospectral cloud identification in Meteosat satellite images
Author(s): Piet Boekaerts; E. Nyssen; Jan P.H. Cornelis
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Paper Abstract

A non-supervised, autoadaptive cloud identification scheme for mono-spectral Meteosat data is presented. The identification of clouds is equivalent to the assignment of meteorological meaningful labels to cloud regions. Automated cloud region detection is reduced to the problem of finding an algorithm that performs a data reduction on Meteosat images while optimally preserving cloud region information. A self-organizing 1D feature map applied to random segments of individual Meteosat channels is shown to meet the requirements of such algorithm. A study of the segment size indicates that small segment sizes are sufficient and even better than large segment sizes for consistent mono-spectral cloud region detection. This is explained in terms of the statistical properties of Meteosat images and the structural features of the code vectors (code segments) in the topological map. Decreasing the number of code segments used to reduce the information content of Meteosat channels results in a systematic, consistent loss of cloud region information.

Paper Details

Date Published: 17 November 1995
PDF: 13 pages
Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226842
Show Author Affiliations
Piet Boekaerts, Vrije Univ. Brussel (Belgium)
E. Nyssen, Vrije Univ. Brussel (Belgium)
Jan P.H. Cornelis, Vrije Univ. Brussel (Belgium)


Published in SPIE Proceedings Vol. 2579:
Image and Signal Processing for Remote Sensing II
Jacky Desachy, Editor(s)

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