24 October 2007 Genetic algorithms in estimating optimal neural network topologies for the classification of remotely sensed images
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Abstract
Neural networks have received much attention in the field of remote sensing. Topology identification remains however one of the major difficulties in the efficient application of neural networks. Currently, topology determination is based on trial and error, on heuristics that amalgamate past experience and on weight pruning algorithms. It is argued in this paper that global search methods such as genetic algorithms can be deployed in discovering near optimal network topologies. An example on multisource classification for land cover mapping is presented. The results indicate that the global search paradigm is worth further exploration especially now that computing becomes more and more powerful.
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Demetris Stathakis, "Genetic algorithms in estimating optimal neural network topologies for the classification of remotely sensed images", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480T (24 October 2007); doi: 10.1117/12.736455; https://doi.org/10.1117/12.736455
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