Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data
- 1 October 1993
- journal article
- research article
- Published by Taylor & Francis in International Journal of Remote Sensing
- Vol. 14 (15) , 2883-2903
- https://doi.org/10.1080/01431169308904316
Abstract
Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-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 classification of very-high-dimcnsional data.Keywords
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