THE USE OF NEURAL NETWORKS IN RARE VEGETATION COMMUNITIES CLASSIFICATION

Authors

  • E. M. Gambarova

DOI:

https://doi.org/10.47839/ijc.7.1.502

Keywords:

Image classification, neural networks, IKONOS

Abstract

This paper describes training of Multilayer Perceptron Neural classifier to extract rare vegetation objects from high spatial resolution IKONOS satellite imagery. There have been considered three options of training of the Multilayer Perceptron Neural according to three different classification schemes. At first 12 type of rare vegetation community types were defined, a main classification scheme (“Initial classification scheme”) was designed on that base. After prelim statistical tests on training samples two modification algorithms of the classification scheme were defined: the first one led to creating of scheme consisting of 7 classes (“Modified classification scheme”) and second one led us to creating of 5-classes scheme (“Optimized classification scheme“). The learning procedures of these classifiers are described as well as analysis and post processing of extraction results of objects of interest using Geoinformation Technologies in details.

References

T. Kavzoglu and P. Mather. The role of feature selection in artificial neural network applications, INT. J. Remote Sensing, 2002, vol. 23, №15, pp. 2919–2937.

M. Bocco, G. Ovando, S. Sayago and E. Willington. Neural network models for land cover classification from satellite images. AGRICULTURA TECNICA, Argentina, 2007, vol. 67, № 4.

C. Oliveira, P. Mather and P. Aplin. Improving artificial neural network performance by temporal-spectral features for agricultural crop classification”. 17th European Simulation multiconference, 9-11 June 2003, Nottingham Trent University, Nottingham, England.

T. Kavzoglu and P. Mather. The use of backpropagating artificial neural networks in land cover classification, INT. J. Remote Sensing, 2003, 24, pp. 4907–4938.

C. Klimasauskas. Applying neural networks, In Neural Networks in Finance and Investing, edited by R. Trippi and E. Turban (Cambridge: Probus), 1993, pp. 47–72.

T. Kavzoglu. An investigation of the design and use of feed-forward artificial neural networks in the classifcation of remotely sensed images, PhD thesis, School of Geography, The University of Nottingham, 2001.

R. Congalton and K. Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, New York, 1999.

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Published

2014-08-01

How to Cite

Gambarova, E. M. (2014). THE USE OF NEURAL NETWORKS IN RARE VEGETATION COMMUNITIES CLASSIFICATION. International Journal of Computing, 7(1), 171-184. https://doi.org/10.47839/ijc.7.1.502

Issue

2008, Volume 7, Issue 1

Section

Articles

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