scholarly journals Band selection and classification of hyperspectral images using Mutual Information: An algorithm based on minimizing the error probability using the inequality of Fano

2012 ◽  
Author(s):  
Elkebir Sarhrouni ◽  
Ahmed Hammouch ◽  
Driss Aboutajdine
Keyword(s):  
Mutual Information ◽  
Error Probability ◽  
Hyperspectral Images ◽  
Band Selection

scholarly journals Spatial Mutual Information Based Hyperspectral Band Selection for Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Anthony Amankwah
Keyword(s):  
Mutual Information ◽  
Supervised Classification ◽  
Band Selection ◽  
Support Vector ◽  
Spatial Dependency ◽  
Information Redundancy ◽  
Spectral Bands ◽  
Validation Criteria ◽  
Selection For

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results.

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