  Polarimetric synthetic aperture radar (PolSAR) has achieved a prominent
position as a remote imaging method. However, PolSAR images are contaminated by
speckle noise due to the coherent illumination employed during the data
acquisition. This noise provides a granular aspect to the image, making its
processing and analysis (such as in edge detection) hard tasks. This paper
discusses seven methods for edge detection in multilook PolSAR images. In all
methods, the basic idea consists in detecting transition points in the finest
possible strip of data which spans two regions. The edge is contoured using the
transitions points and a B-spline curve. Four stochastic distances, two
differences of entropies, and the maximum likelihood criterion were used under
the scaled complex Wishart distribution; the first six stem from the h-phi
class of measures. The performance of the discussed detection methods was
quantified and analyzed by the computational time and probability of correct
edge detection, with respect to the number of looks, the backscatter matrix as
a whole, the SPAN, the covariance an the spatial resolution. The detection
procedures were applied to three real PolSAR images. Results provide evidence
that the methods based on the Bhattacharyya distance and the difference of
Shannon entropies outperform the other techniques.
