Title: Training Strategies for Neural Network Soft Classification of Remotely Sensed Imagery.
Citation: International Journal of Remote Sensing vol. 18 no. 8 p. 1851-1856
Publication Year: 1997
JRC N°: JRC13602
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC13602
Type: Articles in periodicals and books
Abstract: Evidence from the recent literature demonstrates little progress in discrete (hard) classification of land cover from remotely sensed imagery. An empirical study is presented to test training procedures with neural networks for soft (mixture) classification. The results show that land cover mixtures are best recognized following training with two-component mixed pixels, and that linearly re-scaled or binneed target vector representations are equally satisfactory.
JRC Directorate:Joint Research Centre Historical Collection

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