Multispectral Classification of Landsat-Images using Neural Networks

Horst Bischof, Werner Schneider, Axel Pinz

Research output: Contribution to journalArticlepeer-review


The authors report the application of three-layer back-propagation networks for classification of Landsat TM data on a pixel-by-pixel basis. The results are compared to Gaussian maximum likelihood classification. First, it is shown that the neural network is able to perform better than the maximum likelihood classifier. Secondly, in an extension of the basic network architecture it is shown that textural information can be integrated into the neural network classifier without the explicit definition of a texture measure. Finally, the use of neural networks for postclassification smoothing is examined.
Original languageEnglish
Pages (from-to)482-490
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number3
Publication statusPublished - 1992


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