A Method of Pixel Unmixing by Classes based on the Possibilistic Similarity

B. Alsahwa, S. Almouahed, D. Guériot, B. Solaiman

Abstract

In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert’s semantic knowledge (a priori knowledge) and the contextual information. Dubois-Prade’s probability-possibility transformation is used to construct these possibility distributions starting from statistical information (learning areas delimitated by an expert for each thematic class in the analyzed scene) which serve, first, for the estimation of the probability density functions using the kernel density estimation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined thematic classes. The obtained similarity values are used in order to obtain the abundances of different classes in the considered pixel. Accuracy analysis of pixels unmixing demonstrates that the proposed approach represents an efficient estimator of their abundances of the predefined thematic classes and, in turn, higher classification accuracy is achieved. Synthetic images are used in order to evaluate the performances of the proposed approach.

References

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Paper Citation


in Harvard Style

Alsahwa B., Almouahed S., Guériot D. and Solaiman B. (2014). A Method of Pixel Unmixing by Classes based on the Possibilistic Similarity . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 220-226. DOI: 10.5220/0004826202200226


in Bibtex Style

@conference{icpram14,
author={B. Alsahwa and S. Almouahed and D. Guériot and B. Solaiman},
title={A Method of Pixel Unmixing by Classes based on the Possibilistic Similarity},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={220-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004826202200226},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Method of Pixel Unmixing by Classes based on the Possibilistic Similarity
SN - 978-989-758-018-5
AU - Alsahwa B.
AU - Almouahed S.
AU - Guériot D.
AU - Solaiman B.
PY - 2014
SP - 220
EP - 226
DO - 10.5220/0004826202200226