  In this letter, we propose a novel technique for obtaining scattering
components from Polarimetric Synthetic Aperture Radar (PolSAR) data using the
geodesic distance on the unit sphere. This geodesic distance is obtained
between an elementary target and the observed Kennaugh matrix, and it is
further utilized to compute a similarity measure between scattering mechanisms.
The normalized similarity measure for each elementary target is then modulated
with the total scattering power (Span). This measure is used to categorize
pixels into three categories i.e. odd-bounce, double-bounce and volume,
depending on which of the above scattering mechanisms dominate. Then the
maximum likelihood classifier of [J.-S. Lee, M. R. Grunes, E. Pottier, and L.
Ferro-Famil, Unsupervised terrain classification preserving polarimetric
scattering characteristics, IEEE Trans. Geos. Rem. Sens., vol. 42, no. 4, pp.
722731, April 2004.] based on the complex Wishart distribution is iteratively
used for each category. Dominant scattering mechanisms are thus preserved in
this classification scheme. We show results for L-band AIRSAR and ALOS-2
datasets acquired over San Francisco and Mumbai, respectively. The scattering
mechanisms are better preserved using the proposed methodology than the
unsupervised classification results using the Freeman-Durden scattering powers
on an orientation angle (OA) corrected PolSAR image. Furthermore, (1) the
scattering similarity is a completely non-negative quantity unlike the negative
powers that might occur in double- bounce and odd-bounce scattering component
under Freeman Durden decomposition (FDD), and (2) the methodology can be
extended to more canonical targets as well as for bistatic scattering.
