Cluster-based ensemble classification for hyperspectral remote sensing images

Mingmin Chi*, Qun Qian, Jón Atli Benediktsson

*Corresponding author for this work

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

19 Citations (Scopus)

Abstract

Hyperspectral remote sensing images play a very important role in the discrimination of spectrally similar land-cover classes. In order to obtain a reliable classifier, a larger amount of representative training samples are necessary compared to multispectral remote sensing data. In real applications, it is difficult to obtain a sufficient number of training samples for supervised learning. Besides, the training samples may not represent the real distribution of the whole space. To attack the quality problems of training samples, we proposed a Cluster-based ENsemble Algorithm (CENA) for the classification of hyperspectral remote sensing images. Data set collected from ROSIS university validates the effectiveness of the proposed approach.

Original languageEnglish
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
PagesI209-I212
Edition1
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States
Duration: 6 Jul 200811 Jul 2008

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Number1
Volume1

Conference

Conference2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Country/TerritoryUnited States
CityBoston, MA
Period6/07/0811/07/08

Other keywords

  • Ensemble
  • Hyperspectral remote sensing images.
  • Mixture of Gaussian (MoG)
  • Support Cluster Machine (SCM)

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