Nonlinear parsimonious feature selection for the classification of hyperspectral images
| Autor: | Anthony Zullo, Frédéric Ferraty, Mathieu Fauvel |
|---|---|
| Přispěvatelé: | ProdInra, Migration, 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, École nationale supérieure agronomique de Toulouse [ENSAT], Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
nonlinear feature selection
Computer science business.industry [SDV]Life Sciences [q-bio] hyperspectral image classification Feature extraction Hyperspectral imaging Feature selection Pattern recognition Mixture model [SHS]Humanities and Social Sciences parsimony Support vector machine [SDV] Life Sciences [q-bio] Classification rate Nonlinear system ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Full model Artificial intelligence gaussian mixture model [SHS] Humanities and Social Sciences 10. No inequality business |
| Zdroj: | 6. Workshop on Hyperspectral image and signal processing: evolution in remote sensing 6. Workshop on Hyperspectral image and signal processing: evolution in remote sensing, Jun 2014, Lausanne, Switzerland. 4 p WHISPERS |
| Popis: | International audience; A nonlinear parsimonious feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM). GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimate of the correct classification rate. 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 correct classification rate is computed, rather re-estimate the full model. Secondly, using marginalization of the GMM, sub models can be directly obtain from the full model learns with all the spectral features. Experimental results for three hyperspectral data sets show that the method performs very well and is able to extract very few spectral channels. |
| Databáze: | OpenAIRE |
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