%0 Journal Article
%T Fully Polarimetric Land Cover Classification Based on Markov Chains
%A Georgia Koukiou
%A Vassilis Anastassopoulos
%J Advances in Remote Sensing
%P 47-65
%@ 2169-2688
%D 2021
%I Scientific Research Publishing
%R 10.4236/ars.2021.103003
%X A novel
land cover classification procedure is presented utilizing the infor</span><span style=\"font-family:Verdana;\">mation content of fully polarimetric SAR images.
The Cameron cohere</span><span style=\"font-family:Verdana;\">nt target decomposition (CTD) is employed to
characterize land cover pixel by pixel. Cameron¡¯s CTD is employed since it
provides a complete set of elem</span><span style=\"font-family:Verdana;\">entary
scattering mechanisms to describe the physical properties of t</span><span style=\"font-family:Verdana;\">he
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 </span><span style=\"font-family:Verdana;\">scatterers themselves, but the
way these types of scatterers alternate from p</span><span style=\"font-family:Verdana;\">ixel </span><span style=\"font-family:Verdana;\">to pixel on the SAR image. Thus, transition
matrices that represent loc</span><span style=\"font-family:Verdana;\">al 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.
%K Fully Polarimetric SAR
%K Coherent Decomposition
%K Elementary Scatterers
%K Markov Chains
%K Land Cover Classification
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=110928