Abstract
Application of neural networks to classification of remote sensing data is discussed. Convkntional tw+layer backpropagation is found to give good results in ~lassificationo f remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of nsultisource remote sensing and gedgraphic data and veryhigh- dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data but do not compare as well with statistical methods in classifictition of very-high-dimensional data.
Date of this Version
April 1992
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