Novel artificial neural networks for remote-sensing data classification

Xiaoli Tao; Howard E. Michel

doi:10.1117/12.609117

19 May 2005 Novel artificial neural networks for remote-sensing data classification

Xiaoli Tao, Howard E. Michel

Proceedings Volume 5781, Optics and Photonics in Global Homeland Security; (2005) https://doi.org/10.1117/12.609117
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Xiaoli Tao and Howard E. Michel "Novel artificial neural networks for remote-sensing data classification", Proc. SPIE 5781, Optics and Photonics in Global Homeland Security, (19 May 2005); https://doi.org/10.1117/12.609117

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