A Novel Unsupervised Approach for Land Classification Based on Touzi Scattering Vector Model in the Context of Very High Resolution PolSAR Imagery


  • Sheng Sun
  • Jian Gong
  • Zhijia Xu




With the popularization of very high resolution polarimetric
synthetic aperture radar image dataset, it is essential
to re-investigate the classification scheme for 2-D land cases. The
Touzi scattering vector model, a unique and roll-invariant decomposition
solution, is employed to extract the scattering properties
of different land covers. The parameters of Touzi decomposition
act as input dataset for initial classification. A novel classifying
algorithm is put forward by means of integrating the Touzi decomposition
with conventional Wishart statistical models. Quantitative
experiments are then conducted using uninhabited aerial
vehicle synthetic aperture radar sample data for evaluating the
performance of this new proposed approach. It can be concluded
from the experimental results that the new proposed method is superior
to the classical method in terms of producer accuracy, user
accuracy, and overall accuracy.