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Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data

Benediktsson, J.A., Swain, P.H., Ersoy, O.K.·1990·IEEE Transactions on Geoscience and Remote Sensing

Abstract

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

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Crossref · 10.1109/tgrs.1990.572944

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Metadata

Authors
Benediktsson, J. A., Swain, P. H., & Ersoy, O. K.
Venue
IEEE Transactions on Geoscience and Remote Sensing
Published
1990-07
Volume
28
Issue
4
Pages
540-552
ISSN
0196-2892 (ISSN-L)
OpenAlex
W2144841545
Cited by
945
References
1
FWCI
67.36
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data. https://app.cochat.ai/papers/10.1109/tgrs.1990.572944
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Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1990). Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.1990.572944

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