OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

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

Lagrange, Adrien and Fauvel, Mathieu and Grizonnet, Manuel Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images. (2017) IEEE Transactions on Computational Imaging, 23 (2). 230-242. ISSN 2333-9403

[img]
Preview
(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
605kB

Official URL: http://dx.doi.org/10.1109/TCI.2017.2666551

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.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org The original PDF can be found at IEEE Transactions on Computational Imaging (ISSN 2333-9403) website : http://ieeexplore.ieee.org/document/7847352/ Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National d'Études Spatiales - CNES (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Institut National de la Recherche Agronomique - INRA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Funders:
10.13039/501100001665-French National Research Agency (ANR)
Statistics:download
Deposited By: IRIT IRIT
Deposited On:03 Apr 2018 14:14

Repository Staff Only: item control page