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

Shuji Kawaguchi; Ryuei Nishii

doi:10.1109/IGARSS.2006.241

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

Shuji Kawaguchi, Ryuei Nishii

Division of Advanced Mathematics Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 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 language	English
Title of host publication	2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	928-931
Number of pages	4
ISBN (Print)	0780395107, 9780780395107
DOIs	https://doi.org/10.1109/IGARSS.2006.241
Publication status	Published - 2006
Event	2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, United States Duration: Jul 31 2006 → Aug 4 2006

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other	2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Country/Territory	United States
City	Denver, CO
Period	7/31/06 → 8/4/06

All Science Journal Classification (ASJC) codes

Computer Science Applications
Earth and Planetary Sciences(all)

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10.1109/IGARSS.2006.241

Cite this

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. 928-931). [4241388] (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2006.241

Hyperspectral image classification by AdaBoost with decision stumps based on composed feature variables. / Kawaguchi, Shuji; Nishii, Ryuei.
2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Institute of Electrical and Electronics Engineers Inc., 2006. p. 928-931 4241388 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 4241388, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 928-931, 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Denver, CO, United States, 7/31/06. https://doi.org/10.1109/IGARSS.2006.241

Kawaguchi S, Nishii R. Hyperspectral image classification by AdaBoost with decision stumps based on composed feature variables. In 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Institute of Electrical and Electronics Engineers Inc. 2006. p. 928-931. 4241388. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2006.241

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

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