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|dc.identifier.citation||Intern. Journal of Remote Sensing vol. 18 no. 4 p. 711-725||en_GB|
|dc.description.abstract||This paper examines a number of experimental investigations of neural networks used for the classification of remotely sensed satellite imagery at the Joint Research Centre over a period of five years and attemps to draw some conclusions about "best practice" techniques to optimize network training and overall classification performance. The paper examines best practice in such areas as: network architecture selection, use of optimization algorithms, scaling of input data, avoidance of chaos effects, use of enhanced feature sets, and use of hybrid classifier methods. It is concluded that a vast body of accumulated experience is now available and that neural networks can be used reliably and with much confidence for routine operational requirements in remote sensing.||en_GB|
|dc.description.sponsorship||JRC.(SAI)-Space Application Institute||en_GB|
|dc.title||Strategies and Best Practice for Neural Network Image Classification.||en_GB|
|dc.type||Articles in periodicals and books||en_GB|
|JRC Directorate:||Joint Research Centre Historical Collection|
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