  Multispectral images acquired by satellites are used to study phenomena on
the Earth's surface. Unsupervised classification techniques analyze
multispectral image content without considering prior knowledge of the observed
terrain; this is done using techniques which group pixels that have similar
statistics of digital level distribution in the various image channels. In this
paper, we propose a methodology for unsupervised classification based on a
deterministic cellular automaton. The automaton is initialized in an
unsupervised manner by setting seed cells, selected according to two criteria:
to be representative of the spatial distribution of the dominant elements in
the image, and to take into account the diversity of spectral signatures in the
image. The automaton's evolution is based on an attack rule that is applied
simultaneously to all its cells. Among the noteworthy advantages of
deterministic cellular automata for multispectral processing of satellite
imagery is the consideration of topological information in the image via seed
positioning, and the ability to modify the scale of the study.
