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16 September 1992 Comparative performance of artificial neural networks and conventional methods for multispectral image fusion
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Abstract
This paper compares the performance of an artificial neural network technique to that of two conventional techniques in fusing (classifying) multispectral imagery. The true classification error rate is estimated by use of the k-fold cross-validation technique for a Bayesian classifier, a binary tree classifier, and a backpropagation neural network. The cascade correlation neural network is also described and its theory of operation is compared to that of the backpropagation neural network.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joseph H. Kagel "Comparative performance of artificial neural networks and conventional methods for multispectral image fusion", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140002
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