In this paper Autoassociative Neural Networks (AANN) are used to implement Nonlinear Principal Component Analysis (NLPCA) for dimension reduction of hyperspectral data. The nonlinear components are then considered as inputs for a Multi-Layer Perceptron (MLP) network to perform pixel-based classification. The methodology has been applied considering the test area of Tor Vergata - Frascati, Italy, and the hyperspectral data provided by the CHRIS-PROBA mission. Comparative analysis with a similar procedure considering a more standard dimensionality reduction technique such as Principal Component Analysis (PCA) has been carried out
Licciardi, G., DEL FRATE, F., Duca, R. (2009). Feature reduction of hyperspectral data using autoassociative neural networks algorithms. In Proceedings of International Geoscience and Remote Sensing Symposium (pp.I-176-I-179). IEEE [10.1109/IGARSS.2009.5416882].
Feature reduction of hyperspectral data using autoassociative neural networks algorithms
DEL FRATE, FABIO;
2009-01-01
Abstract
In this paper Autoassociative Neural Networks (AANN) are used to implement Nonlinear Principal Component Analysis (NLPCA) for dimension reduction of hyperspectral data. The nonlinear components are then considered as inputs for a Multi-Layer Perceptron (MLP) network to perform pixel-based classification. The methodology has been applied considering the test area of Tor Vergata - Frascati, Italy, and the hyperspectral data provided by the CHRIS-PROBA mission. Comparative analysis with a similar procedure considering a more standard dimensionality reduction technique such as Principal Component Analysis (PCA) has been carried outI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
