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.
|Seiten (von - bis)||482-490|
|Fachzeitschrift||IEEE Transactions on Geoscience and Remote Sensing|
|Publikationsstatus||Veröffentlicht - 1992|