Abstract
This paper presents a new symbolic classifier based on a region oriented approach. Concerning the learning step, each class is described by a region (or a set of regions) in R p defined by the convex hull of the objects belonging to this class. In the allocation step, the assignment of a new object to a class is based on a dissimilarity matching function which compares the class description (a region or a set of regions) with a point in R p. To show the usefulness of this approach, experiments with simulated SAR images were considered. The evaluation of the proposed classifier is based on the prediction accuracy and it is achieved in the framework of a Monte Carlo experience.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bock, H.H., Diday, E.: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (2000)
De Carvalho, F.A.T., Anselmo, C.A.F., Souza, R.M.C.R.: Symbolic approach to classify large data sets. In: Kiers, H.A.L., et al. (eds.) Data Analysis, Classification, and Related Methods, pp. 375–380. Springer, Heidelberg (2000)
Frery, A.C., Mueler, H.J., Yanasse, C.C.F., Sant’ana, S.J.S.: A model for extremely heterogeneous clutter. IEEE Transactions on Geoscience and Remote Sensing 1, 648–659 (1997)
Ichino, M., Yaguchi, H., Diday, E.: A fuzzy symbolic pattern classifier. In: Diday, E., et al. (eds.) Ordinal and Symbolic Data Analysis, pp. 92–102. Springer, Berlin (1996)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall International Editions, Englewood Cliffs (1988)
Lee, J.S.: Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing 17, 24–32 (1981)
O’Rourke, J.: Computational Geometry in C, 2nd edn. Cambridge University Press, Cambridge (1998)
Souza, R.M.C.R., De Carvalho, F.A.T., Frery, A.C.: Symbolic approach to SAR image classification. In: IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburgo, pp. 1318–1320 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
D’Oliveira Junior, S.T., de A.T. de Carvalho, F., de Souza, R.M.C.R. (2004). Classification of SAR Images Through a Convex Hull Region Oriented Approach. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_118
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive