Wavelet neural networks for supervised training of multispectral data and classification of soil moisture

Chih-Cheng Hung; Kai Qian; Tommy L. Coleman

doi:10.1117/12.454186

28 January 2002 Wavelet neural networks for supervised training of multispectral data and classification of soil moisture

Chih-Cheng Hung, Kai Qian, Tommy L. Coleman

Author Affiliations +

Proceedings Volume 4542, Remote Sensing for Agriculture, Ecosystems, and Hydrology III; (2002) https://doi.org/10.1117/12.454186
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

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.

Citation Download Citation

Chih-Cheng Hung, Kai Qian, and Tommy L. Coleman "Wavelet neural networks for supervised training of multispectral data and classification of soil moisture", Proc. SPIE 4542, Remote Sensing for Agriculture, Ecosystems, and Hydrology III, (28 January 2002); https://doi.org/10.1117/12.454186

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available