Multiple spectral spatial classification approach for hyperspectral data

Yuliya Tarabalka*, Jón Atli Benediktsson, Jocelyn Chanussot, James C. Tilton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

214 Citations (Scopus)


A new multiple-classifier approach for spectralspatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region with a corresponding class label. We propose to use spectralspatial classifiers at the preliminary step of the marker-selection procedure, each of them combining the results of a pixelwise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectralspatial classification map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies when compared with previously proposed classification techniques.

Original languageEnglish
Article number5570985
Pages (from-to)4122-4132
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11
Publication statusPublished - Nov 2010

Bibliographical note

Funding Information:
Manuscript received December 30, 2009; revised May 11, 2010. Date of publication September 13, 2010; date of current version October 27, 2010. This work was supported in part by the Marie Curie Research Training Network “HYPER-I-NET.” Y. Tarabalka is with the Faculty of Electrical and Computer Engineering, University of Iceland, 107 Reykjavik, Iceland, and also with the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab), Grenoble Institute of Technology (INPG), 38402 Saint-Martin-d’Hères Cedex, France (e-mail:

Other keywords

  • Classification
  • hyperspectral images
  • minimum spanning forest (MSF)
  • multiple classifiers (MCs)
  • segmentation


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