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

2012 ◽  
pp. 248-251 ◽  
Author(s):  
Kitti Koonsanit ◽  
Chuleerat Jaruskulchai ◽  
Apisit Eiumnoh
Keyword(s):  
Dimension Reduction ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Band Selection ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Analysis Technique ◽  
Components Analysis ◽  
Selection For

scholarly journals Principal Components Analysis as enhancement Operator and Compression factor

2019 ◽  
Vol 17 (72) ◽  
pp. 25-33
Author(s):  
KhaIiI Ibrahim Kadhim
Keyword(s):  
Principal Components Analysis ◽  
Gaussian Distribution ◽  
Principal Components ◽  
Visual Analysis ◽  
Projection Pursuit ◽  
Orthogonal Projections ◽  
Simplified Approach ◽  
Projection Index ◽  
Hyper Spectral ◽  
Components Analysis

Principal components analysis (PCA) is effective at  compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. Unfortunately, information content in hyper spectral images does not always coincide with such projections. We propose an application of projection pursuit (pp), which seeks to find a set of projections that are "interesting" in the sense that they deviate from the Gaussian distribution assumption. Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections, a two –step iterative process is followed where we first search for a projection that maximizes a projection index based on the information divergence of the projections estimated probability distribution from the Gaussian distribution and then reduce the rank by projections the data on to the subspace orthogonal to the previous projection . To calculate each projections, we use a simplified approach to maximizing the projection index, which does not require optimization algorithm. It searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. The effectiveness of the  method is demonstrated through simulated examples as well as data from the hyper spectral digital imagery collection experiment and the spatially enhanced broadband and array spectrograph system.

scholarly journals Collagen IV and laminin-1 expression in embryonic mouse lens using principal components analysis technique

2018 ◽  
Vol 271 (2) ◽  
pp. 207-221 ◽  
Author(s):  
O. SIJILMASSI ◽  
J.M. LÓPEZ ALONSO ◽  
M.C. BARRIO ASENSIO ◽  
A. DEL RÍO SEVILLA
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Collagen Iv ◽  
Embryonic Mouse ◽  
Analysis Technique ◽  
Components Analysis ◽  
Mouse Lens ◽  
Laminin 1
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