It is very important to efficiently represent the target scattering characteristics in applications of polarimetric radar remote sensing. Three probability mass functions are introduced in this paper for target representation: using similarity parameters to describe target average scattering mechanism, using the eigenvalues of a target coherency matrix to describe target scattering randomness, and using radar received power to describe target scattering intensity. The concept of cross-entropy is employed to measure the difference between two scatterers based on the probability mass functions. Three parts of difference between scatterers are measured separately as the difference of average scattering mechanism, the difference of scattering randomness and the difference of scattering intensity, so that the usage of polarimetric data can be highly efficient and flexible. The supervised/unsupervised image classification schemes and their simplified versions are established based on the minimum cross-entropy principle. They are demonstrated to have better classification performance than the maximum likelihood classifier based on the Wishart distribution assumption, both in supervised and in unsupervised classification.