LAUSR

ABSTRACT Model-based decomposition techniques are widely used for extraction of information from polarimetric synthetic aperture radar (PolSAR) images. The solution of almost all the model-based decomposition methods is based on an assumption. The reason for this is that these models have more number of unknowns than the number of equations. In this paper, we present a fast alternative approach to solve the conventional three- and four-component model-based decomposition methods. Utilizing the matrix rotation theory, we have proposed coherency matrix transformations by two new rotation matrices. After the proposed transformations, the dependency of two unknowns on element of coherency matrix vanishes, so that the number of unknowns become equal to number of equations. This makes the proposed decomposition techniques computationally efficient and more suitable for the analysis of high resolution images where the number of pixels are quite large.