Classification of forest structure using very high resolution Pleiades image texture
CHEHATA, Nesrine
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de recherche pour le développement [IRD [Tunisie]]
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Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de recherche pour le développement [IRD [Tunisie]]
CHEHATA, Nesrine
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de recherche pour le développement [IRD [Tunisie]]
< Reduce
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de recherche pour le développement [IRD [Tunisie]]
Language
en
Communication dans un congrès
This item was published in
IGARSS 2014, International Geoscience and Remote Sensing Symposium, 2014-07-13, Québec. 2014
IEEE
English Abstract
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 ...Read more >
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.Read less <
Keywords
Pléiades
texture
English Keywords
feature selection
classification
forest
Origin
Hal imported