Toward an Optimal Fusion Scheme for Multisource Vegetation Classification - Archive ouverte HAL
Communication Dans Un Congrès Année : 2011

Toward an Optimal Fusion Scheme for Multisource Vegetation Classification

Robin Pouteau
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  • PersonId : 1168503
Cédric Lardeux
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benoit Stoll
Sébastien Chabrier

Résumé

The accuracy of forest classification is generally improved by multisensor data fusion since tree species identification benefits from complementary information. However, hypothesizing multisource fusion can also deteriorate accuracy when a non-relevant source is added, we propose a fusion method for classes in difficulty. When the difficulty threshold we introduce is appropriated, our method outperforms the classical approach consisting in performing fusion for all classes. Moreover, the fusion processing time can widely decrease when several classes are put aside. This method can be used effectively to enhance accuracy and processing speed when analyzing the wealth of information available from remote sensors.
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Dates et versions

hal-03791072 , version 1 (29-09-2022)

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  • HAL Id : hal-03791072 , version 1

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Robin Pouteau, Cédric Lardeux, benoit Stoll, Sébastien Chabrier. Toward an Optimal Fusion Scheme for Multisource Vegetation Classification. 34th International Symposium on Remote Sensing of Environment, Apr 2011, sydney, Australia. ⟨hal-03791072⟩

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