Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.