Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks

Abstract

Hyperspectral sensors provide a large amount of data. The inherent characteristics of hyperspectral feature space still require the development of information extraction algorithms with a high degree of accuracy. Data fusion techniques can enable us to analyze high-dimensional data that is provided by hyperspectral sensors. There are two levels of fusion… (More)
DOI: 10.1109/36.763300

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Cite this paper

@article{Jimenez1999ClassificationOH, title={Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks}, author={Luis O. Jimenez and Anibal Morales-Morell and Antonio Creus}, journal={IEEE Trans. Geoscience and Remote Sensing}, year={1999}, volume={37}, pages={1360-1366} }