1. A method for extracting information of interest from multi-dimensional, multi-parametric or multi-temporal datasets concerning a single object being observed by data fusion, the method comprising: providing a plurality of different data sets for a same object, having data related to one or more of different parameters or different times of acquisition of said parameters; subjecting said data sets to a first processing step by Principal Component Analysis (PCA), which yields an identical number of data sets with transformed data represented by a combination of features; and combining each of said data sets non-linearly with a corresponding transformed data set, thereby providing a certain predetermined number of combination data sets, using weighting parameters; wherein the weighting parameters are established in an empirical and experimental manner using training data sets, and wherein the training data sets are used to determine the non-linear weighting parameter values, which maximize values of new features associated with data of interest, as compared with values of other data.