Neural network parameters affecting image classification
Keywords: Remote sensing, Digital image classification, Artificial neural network technique, Knowledge based classification techniques, Fuzzy techniques, Multi band remote sensing data
AbstractThe study is to assess the behaviour and impact of various neural network parameters and their effects on the classification accuracy of remotely sensed images which resulted in successful classification of an IRS-1B LISS II image of Roorkee and its surrounding areas using neural network classification techniques. The method can be applied for various defence applications, such as for the identification of enemy troop concentrations and in logistical planning in deserts by identification of suitable areas for vehicular movement. Five parameters, namely training sample size, number of hidden layers, number of hidden nodes, learning rate and momentum factor were selected. In each case, sets of values were decided based on earlier works reported. Neural network-based classifications were carried out for as many as 450 combinations of these parameters. Finally, a graphical analysis of the results obtained was carried out to understand the relationship among these parameters. A table of recommended values for these parameters for achieving 90 per cent and higher classification accuracy was generated and used in classification of an IRS-1B LISS II image. The analysis suggests the existence of an intricate relationship among these parameters and calls for a wider series of classification experiments as also a more intricate analysis of the relationships.
How to Cite
Tiwari, K. (2002). Neural network parameters affecting image classification. Defence Science Journal, 51(3), 263-278. https://doi.org/10.14429/dsj.51.2237
Special Issue Papers
Copyright (c) 2016 Defence Science Journal
where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India