Dimensionality reduction of hyperspectral data: Assessing the performance of Autoassociative Neural Networks

Feature extraction for the dimensionality reduction of hyperspectral data is performed by means of Auto-Associative Neural Networks. The algorithm performance is compared to the Principal Component Analysis and the Maximum Noise Fraction ones. Results of land cover pixel-based maps yielded by the reduced vector and a dedicated neural network classification algorithm are also reported.