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Network-based correlated correspondence for unsupervised domain adaptation of hyperspectral satellite images

Abstract : Adapting a model to changes in the data distribution is a relevant problem in machine learning and pattern recognition since such changes degrade the performances of classifiers trained on undistorted samples. This paper tackles the problem of domain adaptation in the context of hyperspectral satellite image analysis. We propose a new correlated correspondence algorithm based on network analysis. The algorithm finds a matching between two distributions, which preserves the geometrical and topological information of the corresponding graphs. We evaluate the performance of the algorithm on a shadow compensation problem in hyperspectral image analysis: the land use classification obtained with the compensated data is improved
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https://hal.archives-ouvertes.fr/hal-01018700
Contributor : Nicolas Courty <>
Submitted on : Friday, July 4, 2014 - 4:50:25 PM
Last modification on : Thursday, April 2, 2020 - 1:54:39 AM
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  • HAL Id : hal-01018700, version 1

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Julien Rebetez, Devis Tuia, Nicolas Courty. Network-based correlated correspondence for unsupervised domain adaptation of hyperspectral satellite images. International Conference on Pattern Recognition (ICPR 2014), Aug 2014, Stockholm, Sweden. pp.1--6. ⟨hal-01018700⟩

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