Abstract: A method for characterizing a sample using spectral images of the sample. At least one volume of values of an observed parameter is generated from the images for a plurality of coordinates of the pixels of the images and a plurality of acquisitions. At least one set of input data from the volume is extracted, with the input data corresponding to the values of the parameter, for a pixel of given coordinates in various acquisitions, to which values at least one conversion function has been applied. The at least one neural network is trained using the input data in order to extract therefrom at least one feature of the sample to be characterized. The at least one feature extracted by the neural network is used to perform a classification of the input data into a plurality of classes, each class being representative of at least one feature of the sample.

Abstract: A method for characterizing a sample using spectral images of the sample. At least one volume of values of an observed parameter is generated from the images for a plurality of coordinates of the pixels of the images and a plurality of acquisitions. At least one set of input data from the volume is extracted, with the input data corresponding to the values of the parameter, for a pixel of given coordinates in various acquisitions, to which values at least one conversion function has been applied. The at least one neural network is trained using the input data in order to extract therefrom at least one feature of the sample to be characterized. The at least one feature extracted by the neural network is used to perform a classification of the input data into a plurality of classes, each class being representative of at least one feature of the sample.