Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data
作者:
J. A. BENEDIKTSSON,
P. H. SWAIN,
O. K. ERSOY,
期刊:
International Journal of Remote Sensing
(Taylor Available online 1993)
卷期:
Volume 14,
issue 15
页码: 2883-2903
ISSN:0143-1161
年代: 1993
DOI:10.1080/01431169308904316
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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.
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