Hyperspectral target detection using independent component analysis based linear mixture model

2007 ◽  
Author(s):  
E. Sarigul ◽  
M. S. Alam
Keyword(s):  
Independent Component Analysis ◽  
Mixture Model ◽  
Target Detection ◽  
Component Analysis ◽  
Independent Component ◽  
Linear Mixture Model

Hyperspectral Blind Unmixing and Multiple Target Detection Using Linear Mixture Model

2012 ◽  
Vol 488-489 ◽  
pp. 1224-1228
Author(s):  
Qaisar Mushtaq ◽  
Ihsan Ul Haq ◽  
Muhammad Ahmad ◽  
Muhammad Sohaib
Keyword(s):  
Comparative Study ◽  
Blind Source Separation ◽  
Mixture Model ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Component Analysis ◽  
Separation Technique ◽  
Multiple Target ◽  
Linear Mixture Model ◽  
Vertex Component Analysis

In this paper a blind source separation technique Joint Approximate Diagonalization of Eigen-matrices (JADE) is investigated to unmixing and multiple target detection for hyperspectral imagery data. Our targeted minerals are Alunite, Buddingtonite, Calcite and Kaolinite in ‘Cuprite’ scene data that has been widely used for research experiments in hyperspectral imagery. A comparative study is conducted to show the effectiveness of the JADE with Vertex Component Analysis. The results are evaluated with both full and reduced bands.

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