Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data

J. A. Benediktsson, P. H. Swain, O. K. Ersoy

Rannsóknarafurð: Framlag til fræðitímaritsGreinritrýni

89 Tilvitnanir (Scopus)

Útdráttur

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 multisourcc remote sensing and geographic data and very-highdimensional 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.

Upprunalegt tungumálEnska
Síður (frá-til)2883-2903
Síðufjöldi21
FræðitímaritInternational Journal of Remote Sensing
Bindi14
Númer tölublaðs15
DOI
ÚtgáfustaðaÚtgefið - okt. 1993

Athugasemd

Funding Information:
This research was supported in part by the National Aeronautics and Space Administration (NASA) through Grant No. NAGW-925, the Icelandic Council of Science and the Research Fund of the University of Iceland.

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