Aspects of multivariate statistical theorywith the application to change detection

This paper proposes a new method for change detection measurement including whole SAR imaging modes such as PolIn- SAR, partial PolInSAR and InSAR in a set of multi-temporal multidimensional SAR images. The method is based on the special case of Kullback-Leibler (KL-divergence) test, known as Mutual Information. In order to develop an algorithm, firstly the joint distribution of PolInSAR data set, based on the second order statistics has been derived. Such a derivation accounts for the whole multi-temporal SAR images. Then the mutual information is used to measure the difference between the joint density of multi-temporal PolSAR data sets and their marginal density known as complex Wishart distribution. A comparison between the proposed and the other well-known change detection (e.g. cross correlation) technique is shown by means of real data, describing the advantages due to the fact that the proposed change detector involves almost every facet of the applied change detection.