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

Shuji Kawaguchi, Ryuei Nishii

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
出版社Institute of Electrical and Electronics Engineers Inc.
ページ928-931
ページ数4
ISBN(印刷版)0780395107, 9780780395107
DOI
出版ステータス出版済み - 2006
イベント2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, 米国
継続期間: 7月 31 20068月 4 2006

出版物シリーズ

名前International Geoscience and Remote Sensing Symposium (IGARSS)

その他

その他2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
国/地域米国
CityDenver, CO
Period7/31/068/4/06

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • 地球惑星科学(全般)

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