Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model

Abstract EN

For improving the accuracy of unsupervised classification based on scattering models, the four-component Yamaguchi model is introduced, which is an improved version of the best-known three-component Freeman model.

Therewith, the four-component model is combined with the Wishart distance model.

The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed.

In experiments, seven areas of various homogeneities are singled out from the Flevoland sample image in AIRSAR dataset.

Qualitative and quantitative experiments are performed for a comparative study.

It can be easily seen that the resolution and details are remarkably upgraded by the new proposed method.

The accuracy of classification in homogeneous areas has also increased significantly by adopting the new iterative algorithm.