Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images

Abstract : A large scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and block matrix. The criterion function is split into two parts : one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time.
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IEEE Transactions on Computational Imaging, 2017, 〈10.1109/TCI.2017.2666551〉
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Soumis le : vendredi 10 mars 2017 - 09:32:41
Dernière modification le : mercredi 15 mars 2017 - 01:08:26

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Adrien Lagrange, Mathieu Fauvel, Manuel Grizonnet. Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images. IEEE Transactions on Computational Imaging, 2017, 〈10.1109/TCI.2017.2666551〉. 〈hal-01382500v4〉

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