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Proceedings Paper

Dimensionality reduction of hyperspectral images using kernel ICA
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Paper Abstract

Computational burden due to high dimensionality of Hyperspectral images is an obstacle in efficient analysis and processing of Hyperspectral images. In this paper, we use Kernel Independent Component Analysis (KICA) for dimensionality reduction of Hyperspectraql images based on band selection. Commonly used ICA and PCA based dimensionality reduction methods do not consider non linear transformations and assumes that data has non-gaussian distribution. When the relation of source signals (pure materials) and observed Hyperspectral images is nonlinear then these methods drop a lot of information during dimensionality reduction process. Recent research shows that kernel-based methods are effective in nonlinear transformations. KICA is robust technique of blind source separation and can even work on near-gaussina data. We use Kernel Independent Component Analysis (KICA) for the selection of minimum number of bands that contain maximum information for detection in Hyperspectral images. The reduction of bands is basd on the evaluation of weight matrix generated by KICA. From the selected lower number of bands, we generate a new spectral image with reduced dimension and use it for hyperspectral image analysis. We use this technique as preprocessing step in detection and classification of poultry skin tumors. The hyperspectral iamge samples of chicken tumors used contain 65 spectral bands of fluorescence in the visible region of the spectrum. Experimental results show that KICA based band selection has high accuracy than that of fastICA based band selection for dimensionality reduction and analysis for Hyperspectral images.

Paper Details

Date Published: 28 April 2009
PDF: 8 pages
Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 731510 (28 April 2009); doi: 10.1117/12.820004
Show Author Affiliations
Asif Khan, National Univ. of Science and Technology (Pakistan)
Intaek Kim, Myongji Univ. (Korea, Republic of)
Seong G. Kong, Temple Univ. (United States)


Published in SPIE Proceedings Vol. 7315:
Sensing for Agriculture and Food Quality and Safety
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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