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Proceedings Paper

A Hierarchical Method Of Remotely Sensed Multispectral High Resolution Image Classification
Author(s): H. Lin; D. Vidal-Madjar
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Paper Abstract

One of the simplest classification algorithm which utilizes a linear discriminant function is known as the minimum distance classifier, it is widely used in pattern recognition. But, it encounters the problem of useless dimension compensation when the feature dimensionality is very large. This is the situation when one wants to use textural features as input parameters for classification as it is now possible with the remotely sensed high resolution images (optical or radar). To avoid this problem, we propose a hierarchical classification algorithm.

Paper Details

Date Published: 14 October 1987
PDF: 3 pages
Proc. SPIE 0804, Advances in Image Processing, (14 October 1987); doi: 10.1117/12.941295
Show Author Affiliations
H. Lin, Centre de Recherches en Physique de l'Environnement (CNET/CRPE) (France)
D. Vidal-Madjar, Centre de Recherches en Physique de l'Environnement (CNET/CRPE) (France)


Published in SPIE Proceedings Vol. 0804:
Advances in Image Processing
Andre J. Oosterlinck; Andrew G. Tescher, Editor(s)

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