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Proceedings Paper

Wavelet neural networks for supervised training of multispectral data and classification of soil moisture
Author(s): Chih-Cheng Hung; Kai Qian; Tommy L. Coleman
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Paper Abstract

Artificial neural networks (ANN) constitute a powerful class of nonlinear function approximates for model-free estimation. Neural network models are characterized by topology, activation function and learning rules. The wavelet neuron model is obtained by replacing an activation function with wavelet bases in the traditional neuron model. The wavelet is a localized function that is capable of detecting some features in signals. A wavelet basis function is assigned for each neuron and each synaptic weight is determined by learning. Wavelet neural networks are used in this study to process remotely sensed data and classify soil based on its moisture content. To evaluate the effectiveness of the wavelet neural networks, a soil moisture data set consisting of 750 vectors, each with three components (surface temperature, brightness temperature at L-Band (TB-L) and at S-Band (TB-S)) and some remotely sensed images are evaluated in the experiments. A comparison with Backpropagation networks is investigated for the supervised training of remotely sensed data and classification of soil moisture.

Paper Details

Date Published: 28 January 2002
PDF: 8 pages
Proc. SPIE 4542, Remote Sensing for Agriculture, Ecosystems, and Hydrology III, (28 January 2002); doi: 10.1117/12.454186
Show Author Affiliations
Chih-Cheng Hung, Southern Polytechnic State Univ. (United States)
Kai Qian, Southern Polytechnic State Univ. (United States)
Tommy L. Coleman, Alabama A&M Univ. (United States)

Published in SPIE Proceedings Vol. 4542:
Remote Sensing for Agriculture, Ecosystems, and Hydrology III
Manfred Owe; Guido D'Urso, Editor(s)

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