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1

Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba-City, Ibaraki, 305-8506, Japan

2

Institute of Industrial Science, University of Tokyo, Meguro-ku, Tokyo, 153-8505, Japan

3

-Department of Mathematics, University of Texas-Pan American, Edinburg, Texas 78539, USA





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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. View Full-Text

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





Autor: Hasi Bagan 1, Wataru Takeuchi 2, Yoshiki Yamagata 1, Xiaohui Wang 3 and Yoshifumi Yasuoka 1

Fuente: http://mdpi.com/



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