A stochastic minimum spanning forest approach for spectral-spatial classification of hyperspectral images

K. Bernard*, Y. Tarabalka, J. Angulo, J. Chanussot, J. A. Benediktsson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

A new method for supervised hyperspectral data classification is proposed. In particular, the notion of Stochastic Minimum Spanning Forests (MSFs) is introduced. For a given hyper-spectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, resulting in a final classification map. The experimental results presented on an AVIRIS image of the vegetation area show that the proposed approach yields accurate classification maps, and thus is attractive for hyperspectral data analysis.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages1265-1268
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sept 201114 Sept 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period11/09/1114/09/11

Other keywords

  • classification
  • Hyperspectral image
  • minimum spanning forest
  • multiple classifiers
  • stochastic markers

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