Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery

Details Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery

Jan 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012adspr..49..135g&link_type=abstract

Advances in Space Research, Volume 49, Issue 1, p. 135-142.

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Scientific paper

In this work a technique for cloud detection and classification from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using order-invariant watershed algorithms, which are applied to the corresponding gradient images, computed by a multi-dimensional morphological operator. To reduce the over-segmentation produced by the watershed method, a RAG (Region Adjacency Graph) based region merging technique is applied, using region dissimilarity functions. Once the objects present in the image have been segmented, they are classified using a multi-threshold method based on physical considerations that takes into account the statistical parameters inside each region.

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