A novel
land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images.
The Cameron coherent target decomposition (CTD) is employed to
characterize land cover pixel by pixel. Cameron’s CTD is employed since it
provides a complete set of elementary
scattering mechanisms to describe the physical properties of the
scatterer. The novelty of the proposed land classification approach lies on the
fact that the features used for classification are not the types of the
elementary scatterers themselves, but the
way these types of scatterers alternate from pixel to pixel on the SAR image. Thus, transition
matrices that represent local Markov models are used as classification
features for land cover classification. The classification rule employs only
the most important transitions for decision making. The Frobenius inner product
is employed as similarity measure. Ten different types of land cover are used
for testing the proposed method. In this aspect, the classification performance
is significantly high.
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