Cloud motion wind (CMW) determination requires tracking of individual cloud targets.
This is achieved by first clustering and then tracking each cloud cluster. Ideally, different
cloud clusters correspond to diiferent pressure levels. Two new clustering techniques
have been developed for the identification of cloud types in multi-spectral satellite imagery.
The first technique is the Global-Local clustering algorithm. It is a cascade of a
histogram clustering algorithm and a dynamic clustering algorithm. The histogram
clustering algorithm divides the multi-spectral histogram into'non-overlapped regions,
and these regions are used to initialise the dynamic clustering algorithm. The dynamic
clustering algorithm assumes clusters have a Gaussian distributed probability density
function with diiferent population size and variance.
The second technique uses graph theory to exploit the spatial information which is
often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering
and spectral clustering. The first stage extracts homogeneous objects in the image
using a family of algorithms based on stepwise optimization. This family of algorithms
can be further divided into two approaches: Top-down and Bottom-up. The second
stage groups similar segments into clusters using a statistical hypothesis test on their
similarities. The clusters generated are less noisy along class boundaries and are in
hierarchical order. A criterion based on mutual information is derived to monitor the
spatial clustering process and to suggest an optimal number of segments.
An automated cloud motion tracking program has been developed. Three images
(each separated by 30 minutes) are used to track cloud motion and the middle image
is clustered using Global-Local clustering prior to tracking. Compared with traditional
methods based on raw images, it is found that separation of cloud types before cloud
tracking can reduce the ambiguity due to multi-layers of cloud moving at different
speeds and direction. Three matching techniques are used and their reliability compared.
Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The
optimum target size for first generation METEOSAT images has also been found.Meteorological Office, Bracknel
