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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 248-251 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.124

Band Selection for Dimension Reduction in Hyper Spectral Image Using Integrated Information Gain and Principal Components Analysis Technique

Kitti Koonsanit, Chuleerat Jaruskulchai, and Apisit Eiumnoh

Abstract—Nowadays, hyper spectral image software becomes widely used. Although hyper spectral images provide abundant information about bands, their high dimensionality also substantially increases the computational burden. An important task in hyper spectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details. In this paper, we present band selection technical using principal components analysis (PCA) and information gain (IG) for hyper spectral image such as small multi-mission satellite (SMMS). Band selection method in our research not only serves as the first step of hyper spectral data processing that leads to a significant reduction of computational complexity, but also a invaluable research tool to identify optimal spectral for different satellite applications. In this paper, an integrated PCA and IG method is proposed for hyper spectral band selection. Based on tests in a SMMS hyper spectral image, this new method achieves good result in terms of robust clustering.

Index Terms—Band selection, principal components analysis, PCA, satellite image, information gain, IG.

Kitti Koonsanit and Chuleerat Jaruskulchai are with Kasetsart University, Bangkok, Thailand (e-mail: sc431137@hotmail.com).
Apisit Eiumnoh is with National Center for Genetic Engineering and Biotechnology, Patumthani, Thailand.

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Cite: Kitti Koonsanit, Chuleerat Jaruskulchai, and Apisit Eiumnoh, "Band Selection for Dimension Reduction in Hyper Spectral Image Using Integrated Information Gain and Principal Components Analysis Technique," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 248-251, 2012.

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