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Proceedings Paper

Novel artificial neural networks for remote-sensing data classification
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Paper Abstract

This paper discusses two novel artificial neural network architectures applied to multi-class classification problems of remote-sensing data. These approaches are 1) a spiking-neural-network model for the partitioning of data into clusters, and 2) a neuron model based on complex-valued weights (CVN). In the former model, the learning process is based on the Spike Timing-Dependent Plasticity rule under the Hebbian Learning framework. With temporally encoded inputs, the synaptic efficiencies of the delays between the pre- and post-synaptic spikes can store the information of different data clusters. With the encoding method using Gaussian receptive fields, the model was applied to the remote-sensing data. The result showed that it could provide more useful information than using traditional clustering method such as K-means. The CVN model has proved to be more powerful than traditional neuron models in solving the XOR problem and image processing problems. This paper discusses an implementation of the complex-valued neuron in NRBF neural networks to improve the NRBF structure. The complex-valued weights are used in the supervised learning part of an NRBF neural network. This classifier was tested with satellite multi-spectral image data and results show that this neural network model is more accurate and powerful than the conventional NRBF model.

Paper Details

Date Published: 19 May 2005
PDF: 12 pages
Proc. SPIE 5781, Optics and Photonics in Global Homeland Security, (19 May 2005); doi: 10.1117/12.609117
Show Author Affiliations
Xiaoli Tao, Univ. of Massachusetts/Dartmouth (United States)
Howard E. Michel, Univ. of Massachusetts/Dartmouth (United States)

Published in SPIE Proceedings Vol. 5781:
Optics and Photonics in Global Homeland Security
Theodore T. Saito, Editor(s)

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