Auto Adjustable ANN-Based Classification System for Optimal High Dimensional Data Analysis

Abstract

ANN-based supervised classification systems are very popular when dealing with high dimensional datasets, like multi or hyperspectral images. Typical approaches require a highly time-consuming preprocessing stage where the dimensionality is reduced through the deletion or averaging of redundant information and the establishment of a processing “window” that is displaced over the dataset. Only after this stage, the ANN-based system can perform the classification process the success of which, as a consequence, depends on the quality of the preprocessed data. In this paper, we propose a classification system that automatically obtains the optimal window size and dimensional transformation parameters for a given set of categorization requirements while it is performing the training of the ANN. In addition, the parameters of the ANN in terms of number of inputs are also adapted on line. To test the system, it was applied to a hyperspectral image classification process of real materials where the pixel resolution implies that a material is characterized by spectral patterns of combinations of pixels.