Method of extraction of information of interest to multi-dimensional, multi-parametric and/or multi-temporal datasets related to a same object under observation through data fusion, in which a plurality of different data sets are provided concerning a single object, with the data related to various parameters and/or different time acquisition instants of said parameters. The data set are subjected to a first processing step by principal component analysis generated by an identical number of datasets with transformed data; and each of the datasets is combined in non-linearly with the corresponding transformed data set to obtain a certain predetermined number of combinations of parameters by weighing using parameters determined empirically using training datasets which determine the values of the non-linear weighting parameters that maximize the value of the new features associated with the data of interest, as …

Method for extracting information of interest from multi-dimensional, multi-parametric and/or multi-temporal datasets

Silvana Dellepiane;Irene Minetti;Gianni Vernazza
2015

Abstract

Method of extraction of information of interest to multi-dimensional, multi-parametric and/or multi-temporal datasets related to a same object under observation through data fusion, in which a plurality of different data sets are provided concerning a single object, with the data related to various parameters and/or different time acquisition instants of said parameters. The data set are subjected to a first processing step by principal component analysis generated by an identical number of datasets with transformed data; and each of the datasets is combined in non-linearly with the corresponding transformed data set to obtain a certain predetermined number of combinations of parameters by weighing using parameters determined empirically using training datasets which determine the values of the non-linear weighting parameters that maximize the value of the new features associated with the data of interest, as …
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/938025
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