Fast forward feature selection of hyperspectral images for classification with gaussian mixture models
| Autor: | Frédéric Ferraty, Clement Dechesne, Mathieu Fauvel, Anthony Zullo |
|---|---|
| Přispěvatelé: | Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Systèmes d'élevage méditerranéens et tropicaux (UMR SELMET), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université Fédérale Toulouse Midi-Pyrénées |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
nonlinear feature selection
Atmospheric Science business.industry hyperspectral image classification Feature extraction Hyperspectral imaging Pattern recognition Feature selection [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering Mixture model Cross-validation parsimony Support vector machine Classification rate [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Artificial intelligence gaussian mixture model Computers in Earth Sciences gmm business Classifier (UML) Mathematics |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2015, 8 (6), pp.2824-2831. ⟨10.1109/jstars.2015.2441771⟩ |
| ISSN: | 1939-1404 |
| DOI: | 10.1109/jstars.2015.2441771⟩ |
| Popis: | International audience; A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation (k-CV). In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, submodels can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels. |
| Databáze: | OpenAIRE |
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