  Landmark recognition and matching is a critical step in many Image Navigation
and Registration (INR) models for geostationary satellite services, as well as
to maintain the geometric quality assessment (GQA) in the instrument data
processing chain of Earth observation satellites. Matching the landmark
accurately is of paramount relevance, and the process can be strongly impacted
by the cloud contamination of a given landmark. This paper introduces a
complete pattern recognition methodology able to detect the presence of clouds
over landmarks using Meteosat Second Generation (MSG) data. The methodology is
based on the ensemble combination of dedicated support vector machines (SVMs)
dependent on the particular landmark and illumination conditions. This
divide-and-conquer strategy is motivated by the data complexity and follows a
physically-based strategy that considers variability both in seasonality and
illumination conditions along the day to split observations. In addition, it
allows training the classification scheme with millions of samples at an
affordable computational costs. The image archive was composed of 200 landmark
test sites with near 7 million multispectral images that correspond to MSG
acquisitions during 2010. Results are analyzed in terms of cloud detection
accuracy and computational cost. We provide illustrative source code and a
portion of the huge training data to the community.
