Coherence estimation from complex coherence map using second kind statistics

The sample coherence magnitude estimation, computed on a window basis, depends on the number of independent samples and theoretical coherence. Classical methods for sample coherence computation are based on probability density function (pdf) model for estimating regular moments (first kind statistics) defined with the Fourier transform. The proposed approach is based on the same pdf model but for computing "second kind statistics" defined with the Mellin-transform. The result performances of this new log-moment (based on the Mellin-Transform) estimator was presented by the authors in R. Abdelfattah and K.M. Nicolas (2003). This paper presents a generalization of the second kind statistics coherence magnitude estimation approach from a complex coherence maps such as interferometric synthetic aperture radar (InSAR) data. The new developed algorithm is much more less biased than existing ones.