Strategies and best practice for neural network image classification
作者:
I. Kanellopoulos,
G. G. Wilkinson,
期刊:
International Journal of Remote Sensing
(Taylor Available online 1997)
卷期:
Volume 18,
issue 4
页码: 711-725
ISSN:0143-1161
年代: 1997
DOI:10.1080/014311697218719
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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.
点击下载:
PDF (268KB)
返 回