Investigation of Branching Conditions in Model-Based Decomposition Methods

Abstract

In this letter, we investigate the existing branching conditions used to solve the unknown model-coefficients of modelbased decomposition methods and show that they are less efficient in discriminating between dominant surface and dihedral scattering mechanisms. The discrimination ability of the branching conditions further deteriorates when the target has some random slope and orientation. This greatly suppressed the performance of the model-based decomposition methods. To overcome this problem, we propose an efficient alternate to existing branching conditions of model-based methods. The proposed branching condition is based on the value of the alpha (α) angle derived from the eigenvector analysis of the measured coherency matrix. The roll-invariance property of α angle makes it work efficiently even in the sloped and oriented areas. The proposed concept is experimentally validated over three different polarimetric synthetic aperture radar (PolSAR) data sets. The effectiveness of the α angle is analyzed and compared with the other branching conditions in terms of ability to discriminate between dominant surface and dihedral scattering mechanisms. The experimental results on different PolSAR data sets clearly demonstrate that by replacing the existing branching conditions with the α angle, the performances of the model-based decomposition methods are significantly improved.