Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Sensors

Hasi Bagan²  Wataru Takeuchi³  Yoshiki Yamagata²  Xiaohui Wang¹ 

[1] Department of Mathematics, University of Texas-Pan American, Edinburg, Texas 78539, USA; E-mail:;Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki, 305-8506, Japan; E-mails:;Institute of Industrial Science, University of Tokyo, Meguro-ku, Tokyo, 153-8505, Japan; E-mail:

关键词: hyperspectral;    remote sensing;    subspace method;    averaged learning subspace method;    dimension reduction;    land cover;    classification;    normalization;   

DOI  :  10.3390/s90604247

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.