E. Nezry et al., SUPERVISED CLASSIFICATION OF K-DISTRIBUTED SAR IMAGES OF NATURAL TARGETS AND PROBABILITY OF ERROR ESTIMATION, IEEE transactions on geoscience and remote sensing, 34(5), 1996, pp. 1233-1242
A radiometric and textural classification method for the single-channe
l synthetic: aperture radar (SAR) image is proposed, which explicitly
takes into account the probability density function (pdf) of the under
lying cross section for K-distributed images, This method makes extens
ive use of adaptive preprocessing methods (e.g., Gamma-Gamma MAP speck
le filtering), in order to ensure good classification accuracy as well
as fair preservation of the spatial resolution of the final result, E
rror rates can be estimated during the training step, allowing one to
select only relevant reflectivity classes and to save computation time
in trials, The classification method is based on a Maximum Likelihood
(ML) segmentation of the filtered image, Finally, a texture criterion
is introduced to improve the accuracy of the classification result.