Hyperspectral image classification by recursive spatial boosting based on the bootstrap method

Shuji Kawaguchi, Ryuei Nishii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider contextual classification of hyperspectral data based on the boosting method. Bootstrap AdaBoost proposed by Kawaguchi and Nishii (2006) is applied to Spatial Boosting for contextual classification. The paper proposes a recursive version of Spatial Boosting. Posterior probabilities of each pixel are updated by the contextual classification function derived from Spatial Boosting and this is repeated. The proposed method with random stumps shows excellent performance for classification of AVIRIS data. Furthermore, it is superior to other well-known contextual classification methods including MRF-based classifiers.

Original languageEnglish
Title of host publication2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Pages1751-1754
Number of pages4
DOIs
Publication statusPublished - 2007
Event2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain
Duration: Jun 23 2007Jun 28 2007

Other

Other2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
CountrySpain
CityBarcelona
Period6/23/076/28/07

All Science Journal Classification (ASJC) codes

  • Geology
  • Software

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