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Your search keyword '"Zagrouba, Ezzeddine"' showing total 46 results
Start Over Author "Zagrouba, Ezzeddine" Publisher ieee
46 results on '"Zagrouba, Ezzeddine"'

11. Unsupervised Image-Adapted Local Fisher Discriminant Analysis to Reduce Hyperspectral Images Without Ground Truth.

Author

Zaatour, Rania, Bouzidi, Sonia, and Zagrouba, Ezzeddine

Subjects
FISHER discriminant analysis, FEATURE extraction, CELL imaging
Abstract

Local Fisher discriminant analysis (LFDA) is a feature extraction technique that proved efficient to reduce several types of data and succeeded to outperform many state-of-the-art methods. However, due to its supervised nature, LFDA’s efficiency depends on the available labeled samples and declines dramatically when the latter are very few. Hence, we assume that we cannot resort to LFDA to reduce unlabeled data. In this article, we studied to what extent this assumption is true and questioned the possibility of using LFDA to reduce hyperspectral images (HSIs) with no available ground truth. To study the real impact of the labeled information on LFDA’s performance, we replaced the costly expert-made ground truth by different sets of labeled samples that are generated based on the image’s offered spectral and/or spatial information, with no prior knowledge of the captured scene nor of its classes. Our proposed sets proved able to guide LFDA in extracting relevant discriminating features. This proved that LFDA does not depend only on expert-made labeled information and led us to define the unsupervised image-adapted LFDA (uiaLFDA) that can properly reduce an HSI without requiring its ground truth. To do so and to replace the ground truth that LFDA usually requires to reduce an HSI, uiaLFDA defines its own set of labeled samples by simply gridding the image into cells where each cell is considered a class. Our experiments ran on three HSIs proved that uiaLFDA is as efficient as LFDA and, even better, in reducing unlabeled HSIs. [ABSTRACT FROM AUTHOR]

Published
2020
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20. 3-D Shearlet Transform Based Feature Extraction for Improved Joint Sparse Representation HSI Classification.

Author

Zaouali, Mariem, Bouzidi, Sonia, and Zagrouba, Ezzeddine

Abstract

In this paper, a feature extraction approach based on a three-dimensional shearlet transform (shearlet 3D) is proposed. We aim at exploiting shearlet 3D to highlight the intrinsic properties of hyperspectral images (HSIs), well known by their correlated information and high dimensionality. First, we decompose the HSI to yield coefficients arranged in cubes that help the computing of statistical parameters. Afterward, using a simultaneous orthogonal matching pursuit (SOMP) algorithm, a classification process is carried out. SOMP relies on the powerful sparse representation paradigm, which helps representing data in a low-dimensional space. It is also built over the assumption that the contextual information incorporation into the sparse recovery problem improves the classification performance. To this algorithm, we propose to add an adapted decision rule where a similarity measurement is calculated to well assign the appropriate labels to pixels of interest. Experimental results proved that our proposed method outperformed state-of-the-art classifiers. Thanks to our proposed approach, we succeeded to build discriminative descriptors reaching high overall accuracies for two different HSI datasets, without taking into account all the shearlet 3D coefficients. [ABSTRACT FROM PUBLISHER]

Published
2018
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