Extended averaged learning subspace method for hyperspectral data classification

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4247-4270
Number of pages24
Issue number6
Publication statusPublished - 2009 Jun
Externally publishedYes


  • Averaged learning subspace method
  • Classification
  • Dimension reduction
  • Hyperspectral
  • Land cover
  • Normalization
  • Remote sensing
  • Subspace method

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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