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Proceedings Paper

Classification of remote sensing imagery using genetic algorithms and neural networks
Author(s): Graham M. Herries; A. Murray; Sean Danaher; Thomas Selige
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Paper Abstract

This paper presents the application of Neural Networks (ANN) and introduces Genetic Algorithms (GA) to agricultural land use classification. Daedalus ATM data at 1 m resolution, has been used to train and test the algorithms. Layered feed forward ANN's have been found to have good generalization properties. The Backpropagation (BP) algorithm is very susceptible to initial conditions and the problem of local minima. Therefore this technique alone is not the best method for the classification of complex multi-dimensional data sets. This paper applies an evolutionary technique for training feed forward ANN's, which searches the error space for a more likely initialization point. Optimization and learning problems are two techniques where ANN's and GA's have excelled. Evolutionary Artificial Neural Networks, introduced in this paper, can be thought of as being a cross between ANNs and GAs. The weights and biases are updated by applying the mutation genetic operator and can be compared with the principle of natural biological life, where survival of the fittest leads to a near optimum ANN. These weights and biases are then adopted by the BP algorithm to quickly converge on the global minima.

Paper Details

Date Published: 17 November 1995
PDF: 10 pages
Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226836
Show Author Affiliations
Graham M. Herries, Leeds Metropolitan Univ. (United Kingdom)
A. Murray, Leeds Metropolitan Univ. (United Kingdom)
Sean Danaher, Leeds Metropolitan Univ. (United Kingdom)
Thomas Selige, GSF--Ctr. for Environmental and Health Research (Germany)

Published in SPIE Proceedings Vol. 2579:
Image and Signal Processing for Remote Sensing II
Jacky Desachy, Editor(s)

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