  This paper shows in detail the application of a new stochastic approach for
the characterization of surface height profiles, which is based on the theory
of Markov processes. With this analysis we achieve a characterization of the
scale dependent complexity of surface roughness by means of a Fokker-Planck or
Langevin equation, providing the complete stochastic information of multiscale
joint probabilities. The method is applied to several surfaces with different
properties, for the purpose of showing the utility of this method in more
details. In particular we show the evidence of Markov properties, and we
estimate the parameters of the Fokker-Planck equation by pure, parameter-free
data analysis. The resulting Fokker-Planck equations are verified by numerical
reconstruction of conditional probability density functions. The results are
compared with those from the analysis of multi-affine and extended multi-affine
scaling properties which is often used for surface topographies. The different
surface structures analysed here show in details advantages and disadvantages
of these methods.
