This paper presents applications of Synthetic Aperture Radar (SAR) image cl
assification using morphological texture features. The texture features are
based on morphological residues of opening and closing by reconstruction.
It is shown that this set of features shows high 'robustness' to speckle pe
rturbation in SAR images compared with those derived from traditional morph
ological residues. An algorithm based on estimating the divergence between
and within classes was constructed in order to search for a discriminating
feature subset. Higher classification accuracy was obtained by the optimize
d feature subset than by using other feature subsets derived from some well
known texture characterization approaches. The classification accuracy was
continuously improved by the introduction of post-processing filtering.