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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 attempts 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 neutral networks can be used reliably and with much confidence for routine operational requirements in remote sensing.

Additional information

Authors: KANELLOPOULOS I, JRC Ispra (IT);WILKINSON G G, JRC Ispra (IT)
Bibliographic Reference: Article: International Journal of Remote Sensing (1996)
Availability: Available from Dr I Kanellopoulos, Institute for Remote Sensing Applications, Joint Research Centre, 21020 Ispra (IT)
Record Number: 199610310 / Last updated on: 1996-03-29
Category: PUBLICATION
Original language: en
Available languages: en
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