Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Empirical Comparison of Dimensionality Reduction Techniques for Pattern Classification
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Dimensionality Reduction by Learning an Invariant Mapping
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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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.