Feature reduction of hyperspectral data using Autoassociative neural networks algorithms

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.