Strategies and best practice for neural network image classification

Authors: Kanellopoulos I.; Wilkinson G. G.

Source: International Journal of Remote Sensing, Volume 18, Number 4, 1997 , pp. 711-725(15)

Buy & download fulltext article:

Price: $55.77 plus tax (Refund Policy)

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 concludes 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.

Language: English

Document Type: Research article

Publication date: 1997-03-10

More about this publication?