Dr. El-hadi Zahzah Profile

Dr. El-hadi Zahzah

at Univ de La Rochelle

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Publications (2)

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Proceedings Article | 22 December 1997 Paper

Image data fusion by the OWA operators

El-hadi Zahzah

Proceedings Volume 3217, (1997) https://doi.org/10.1117/12.295611

KEYWORDS: Image fusion, Data fusion

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In this paper the Ordered Weighted Averaging OW A operator is introduced related to data cooperation in the field of image analysis. The concept of OW A operator was introduced by Yager in [5], [6] and [7] as a way for providing aggregations which lie between Max and Min. The structure of these operator involves a type of nonlinearity in the form of an ordering operation on the elements to be aggregated. The main difficulty in using this type of operators is to find the appropriate weighted vector. In our approach, we propose to generate the weights by a Neural Network. Furthermore, depending on the dispersion of sources opinions, the most appropriate operator is used taking into account the agreement or the conflict among the sources. In the following we review some basic ideas of the OW A aggregation operator, we then describe the general system operating showing how the coherence degree among the sources are integrated to produce the adaptative operator to any given situation. The method is tested on Landsat multispectral images, using different supervised and non-supervised classification techniques.

Proceedings Article | 17 December 1996 Paper

Mass functions assessment: case of multiple hypothesis for the evidential approach

Michel Menard, El-hadi Zahzah, Ahmad Shahin

Proceedings Volume 2955, (1996) https://doi.org/10.1117/12.262889

KEYWORDS: Electroluminescence, Image fusion, Information fusion, Satellite imaging, Earth observing sensors, Satellites, Lanthanum, Image analysis, Data fusion, Image sensors

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The purpose of this paper is to define the mass functions for a set of multiple mixed hypothesis in an context of Dempster/Shafer (DS) theory which offers an interesting tool to combine data providing from heterogeneous sources more or less reliable by managing imprecision and uncertainty. This is particularly important when dealing with multi-modality imaging (satellite image), where the fusion of information increases the global knowledge about the phenomenon while decreasing the imprecision and uncertainty about it. This theory also enables us to assign masses to 2^D elements (D: decision space) rather than to D elements as in probabilistic theory. The DS has been used in many applications in the field of image analysis, but without its all powerful. When using with only simple hypothesis (an object belongs to only one class), the theory falls in the probabilistic case, which is considered as a particular case. Bloch and Barnett attempt to use double hypothesis but their method still remains particular and restrictive. We propose in this paper a method to extract for a class the consonance and dissonance degrees among several classifiers (methods), and the integration of these terms to initialize the mass functions with multiple mixed hypothesis in order to use the orthogonal Dempster/Shafer Rule. The problem must be viewed from multiclass, multi-sources (images) and multi- point of view (methods or classifiers used) context. We first show how our method works with 1 -- image, 2 -- classifiers, and 2 -- hypothesis and then generalize for P - - images, K -- sources and 2^D -- hypothesis.

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