After a major flood catastrophe, a precious information
is the delineation of the affected areas. Remote sensing
imagery, especially synthetic aperture radar, allows to
obtain a global and complete view of the situation.
However, the detection of the flooded areas remains a
challenge, especially since the reaction time for ground
teams is very short. This makes the application of
automatic detection routines appealing. Such methods must
avoid complex parametrization, heavy computational time
and long intervention by the operator. We propose an
automatic three steps strategy, starting by rebalancing
the different types of pixels (non-water, permanent water
and flooded) using digital elevation model information,
then isolating water pixels and finally separating
flooded from permanent water pixels using non-linear
clustering in dedicated feature spaces. Experiments on
two sets of ASAR images show the effectiveness of the
method competing with supervised standard log-ratio
thresholding.