Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Sensors 2009, 9(6), 4247-4270; doi:10.3390/s90604247
Article

Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

1
, 2
, 1
, 3
 and 1
Received: 14 May 2009; in revised form: 27 May 2009 / Accepted: 1 June 2009 / Published: 3 June 2009
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan)
View Full-Text   |   Download PDF [527 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: 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.
Keywords: hyperspectral; remote sensing; subspace method; averaged learning subspace method; dimension reduction; land cover; classification; normalization hyperspectral; remote sensing; subspace method; averaged learning subspace method; dimension reduction; land cover; classification; normalization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Bagan, H.; Takeuchi, W.; Yamagata, Y.; Wang, X.; Yasuoka, Y. Extended Averaged Learning Subspace Method for Hyperspectral Data Classification. Sensors 2009, 9, 4247-4270.

AMA Style

Bagan H, Takeuchi W, Yamagata Y, Wang X, Yasuoka Y. Extended Averaged Learning Subspace Method for Hyperspectral Data Classification. Sensors. 2009; 9(6):4247-4270.

Chicago/Turabian Style

Bagan, Hasi; Takeuchi, Wataru; Yamagata, Yoshiki; Wang, Xiaohui; Yasuoka, Yoshifumi. 2009. "Extended Averaged Learning Subspace Method for Hyperspectral Data Classification." Sensors 9, no. 6: 4247-4270.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert