BEGUET, Benoît; GUYON, Dominique; CHEHATA, Nesrine; BOUKIR, Samia

doi:10.1109/IGARSS.2014.6946936

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BEGUET, Benoît
Université Sciences et Technologies - Bordeaux 1 [UB]

GUYON, Dominique
Interactions Sol Plante Atmosphère [UMR ISPA]

CHEHATA, Nesrine
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de recherche pour le développement [IRD [Tunisie]]

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IGARSS 2014, International Geoscience and Remote Sensing Symposium, 2014-07-13, Québec. 2014

IEEE

Résumé en anglais

The potential of very high resolution Pléiades image texture for forest structure mapping was assessed on maritime pine stands in south-western France. A preliminary step showed that multi-linear regressions allow a reliable prediction of forest variables (such as crown diameter or tree height) from a set of features automatically selected among a huge number of Haralick texture features with various spatial parameterizations. In a second step, to assess Pléiades image texture contribution for classification, Random Forests (RF) classification was performed to discriminate four forest structure classes from recent reforestation to mature stand. Two texture feature selection strategies are compared: (1) the previous regression-based modelling using in situ tree measurements (2) the RF-variable importance using a visual photo-interpretation. Both methods produced comparable classification accuracies. The results highlight the contribution of processes automation and the need for using both Pléiades image resolutions (panchromatic and multispectral) to derive the best performing texture features.< Réduire

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Pléiades

texture

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feature selection

classification

forest

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Classification of forest structure using very high resolution Pleiades image texture

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