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Proceedings Paper

Feature extraction and pattern classification for remotely sensed data analysis by a modular neural system
Author(s): Palma N. Blonda; Vincenza la Forgia; Guido Pasquariello; Giuseppe Satalino
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Paper Abstract

In this paper a modular neural network architecture is proposed for classification of Remote Sensed data. The neural network learning task of the supervised Multi Layer Perceptron (MLP) Classifier has been made more efficient by pre-processing the input with an unsupervised feature discovery neural module. Two classification experiments have been carried for coping with two different situations, very usual in real remote sensing applications: the availability of complex data, such as high dimensional and multisourced data, and on the contrary, the case of imperfect low dimensional data set, with a limited number of samples. In the first experiment on a multitemporal data set, the Linear Propagation Network (LPN) has been introduced to evaluate the effectiveness of neural data compression stage before classification. In the second experiment on a poor data set, the Kohonen Self Organising Feature Map (SOM) Network has been introduced for clustering data before labelling. In the paper is also illustrated the criterion for the selection of an optimal number of cluster centres to be used as node number of the output SOM layer. The results of the two experiments have confirmed that modular learning performs better than the non-modular one in learning quality and speed.

Paper Details

Date Published: 30 December 1994
PDF: 8 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196743
Show Author Affiliations
Palma N. Blonda, Istituto Elaborazione Segnali ed Immagini (Italy)
Vincenza la Forgia, ALENIA (Italy)
Guido Pasquariello, Istituto Elaborazione Segnali ed Immagini (Italy)
Giuseppe Satalino, ALENIA (Italy)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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