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Proceedings Paper

Texture characterization by new morphological features: application to SPOT image segmentation
Author(s): Wei Li; Veronique Haese-Coat; Kidiyo Kpalma; Joseph Ronsin
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Paper Abstract

Applications of new texture features in SPOT image segmentation are presented in this paper. The set of texture features is based on morphological residues of opening and closing by reconstruction. In texture classification, this set of features is proven much more robust to noise than feature set derived from traditional morphological residues. An optimization algorithm is established to search for the optimum feature subset, and a minimization of window size is evaluated to obtain better classification accuracy. In experiments of various noise circumstances, it is found that this feature set bears quite high texture classification accuracy compared to other texture classification methods. In application of SPOT image segmentation by texture classification, an optimal feature subset with the supervised Gaussian maximum likelihood classifier is employed. To improve the segmentation performances, post- processing is added. Comparisons with other segmentation methods are made.

Paper Details

Date Published: 17 December 1996
PDF: 10 pages
Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); doi: 10.1117/12.262884
Show Author Affiliations
Wei Li, INSA (France)
Veronique Haese-Coat, INSA (France)
Kidiyo Kpalma, INSA (France)
Joseph Ronsin, INSA (France)

Published in SPIE Proceedings Vol. 2955:
Image and Signal Processing for Remote Sensing III
Jacky Desachy, Editor(s)

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