Toward an Optimal Fusion Scheme for Multisource Vegetation Classification - Archive ouverte HAL
Conference Papers Year : 2011

Toward an Optimal Fusion Scheme for Multisource Vegetation Classification

Robin Pouteau
  • Function : Author
  • PersonId : 1168503
Cédric Lardeux
  • Function : Author
  • PersonId : 1168507
benoit Stoll
Sébastien Chabrier

Abstract

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.
Fichier principal
Vignette du fichier
B12 2011 ISRSE34 Fusion.pdf (1.02 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

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

Identifiers

  • HAL Id : hal-03791072 , version 1

Cite

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⟩

Collections

UPF
17 View
17 Download

Share

More