Hyperspectral image classification by AdaBoost with decision stumps based on composed feature variables

Shuji Kawaguchi, Ryuei Nishii

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

2 Citations (Scopus)

Abstract

Over the past few decades, a considerable number of studies have been made on statistical classification methods for hyperspectral imagery. For classification of hyperspectral data, we must take care of a curse of dimension and computation cost. For the problem, we propose AdaBoost by decision stumps based on composed feature variables. We show that the method can be processed in acceptable time for AVIRIS data. The proposed method obtains a more accurate result compared to kernel based NN and SVM. We also assess features of hyperspectral data from the obtained classifiers. The proposed method can imply the relative importance of the feature for classification.

Original languageEnglish
Title of host publication2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Pages936-939
Number of pages4
DOIs
Publication statusPublished - 2006
Event2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, United States
Duration: Jul 31 2006Aug 4 2006

Other

Other2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
CountryUnited States
CityDenver, CO
Period7/31/068/4/06

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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    Kawaguchi, S., & Nishii, R. (2006). Hyperspectral image classification by AdaBoost with decision stumps based on composed feature variables. In 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS (pp. 936-939). [4241388] https://doi.org/10.1109/IGARSS.2006.241