394
20.5
Conclusions
G.M.Foody
Neural networks are powerful general purpose computing tools. They have
become popular in the analysis of remotely sensed data, particularly for
classification and regression-type problems in which they have often been
demonstrated to extract information more accurately than conventional methods.
Although not free from problems, it seems likely that neural networks will be used
increasingly in ecological research using remote sensing. Moreover, as some of
the problems encountered in use of neural networks arise from a tendency to focus
upon the MLP only it is likely that there will be a greater use of other network
types. In addition, it is expected that the range of applications of neural networks
in remote sensing will broaden. Applications in which neural networks have
already been used and increased usage may be expected include: image preprocessing (e.g. geometric, atmospheric and radiometric correction), stereomatching imagery, image compression, feature extraction, map generalisation,
multi-source data analysis, data fusion and image sharpening (e.g. Day 1997;
Foody 1999a). Thus while neural networks have rapidly become established in
remote sensing it is likely that they will be used increasingly and in a broader
range of activities that will help exploit more fully the potential of remote sensing
as a useful tool in ecological research.
Acknowledgements
This article draws upon material presented in aseries of earlier papers and so has
benefited from comment and discussion related to earlier work that is greatly
appreciated.
References
Aleksander I, Morton H (1990) An Introduction to Neural Computing, Chapman and Hall,
London
Atkinson PM, Tatnall ARL (1997) Neural networks in remote sensing, International
Journal of Remote Sensing, 18, 711-725
Baret F (1995) Use of spectral retlectance variation to retrieve canopy biophysical
characteristics, In: Danson, F. M. and Plummer, S. P. (eds) Advances in
Environmental Remote Sensing, Wiley, Chichester, pp. 33-51
Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus
statistical methods in cIassification of multisource remote sensing data, IEEE
Transactions on Geoscience and Remote Sensing, 28, 540-551
20.5
Conclusions
G.M.Foody
Neural networks are powerful general purpose computing tools. They have
become popular in the analysis of remotely sensed data, particularly for
classification and regression-type problems in which they have often been
demonstrated to extract information more accurately than conventional methods.
Although not free from problems, it seems likely that neural networks will be used
increasingly in ecological research using remote sensing. Moreover, as some of
the problems encountered in use of neural networks arise from a tendency to focus
upon the MLP only it is likely that there will be a greater use of other network
types. In addition, it is expected that the range of applications of neural networks
in remote sensing will broaden. Applications in which neural networks have
already been used and increased usage may be expected include: image preprocessing (e.g. geometric, atmospheric and radiometric correction), stereomatching imagery, image compression, feature extraction, map generalisation,
multi-source data analysis, data fusion and image sharpening (e.g. Day 1997;
Foody 1999a). Thus while neural networks have rapidly become established in
remote sensing it is likely that they will be used increasingly and in a broader
range of activities that will help exploit more fully the potential of remote sensing
as a useful tool in ecological research.
Acknowledgements
This article draws upon material presented in aseries of earlier papers and so has
benefited from comment and discussion related to earlier work that is greatly
appreciated.
References
Aleksander I, Morton H (1990) An Introduction to Neural Computing, Chapman and Hall,
London
Atkinson PM, Tatnall ARL (1997) Neural networks in remote sensing, International
Journal of Remote Sensing, 18, 711-725
Baret F (1995) Use of spectral retlectance variation to retrieve canopy biophysical
characteristics, In: Danson, F. M. and Plummer, S. P. (eds) Advances in
Environmental Remote Sensing, Wiley, Chichester, pp. 33-51
Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus
statistical methods in cIassification of multisource remote sensing data, IEEE
Transactions on Geoscience and Remote Sensing, 28, 540-551