Extended averaged learning subspace method for hyperspectral data classification

Hasi Bagan, Wataru Takeuchi, Yoshiki Yamagata, Xiaohui Wang, Yoshifumi Yasuoka

研究成果: Article査読

8 被引用数 (Scopus)

抄録

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations.

本文言語English
ページ(範囲)4247-4270
ページ数24
ジャーナルSensors
9
6
DOI
出版ステータスPublished - 2009 6月
外部発表はい

ASJC Scopus subject areas

  • 分析化学
  • 情報システム
  • 器械工学
  • 原子分子物理学および光学
  • 電子工学および電気工学
  • 生化学

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