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...pping criterion is same fitness value repeated over a long period). B. Binary PSO The basic PSO algorithm starts with a population of random particles, from where the name “particle swarm” is derived =-=[5]-=-. Each particle in PSO is associated with a velocity. Particles’ velocities are adjusted according to the historical behaviour of each particle and its neighbours while they fly through the search spa...

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...defined as n ∑ i= 1 d( x , x ) = ( x − x ) , (7) 1 2 1 where the points are measured on n axes or features. The next two metrics are texture features calculated from the grey level coocurrence matrix =-=[9]-=-, which represents the relative frequency of occurrence of two pixels with grey levels i and j in an image, in the neighborhood of two pixels separated by a distance d in a given direction θ. Given th...

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...assification accuracy [2]. Particle swarm optimization (PSO) is a very promising evolutionary computation technique that had been developed recently for solving nonlinear optimization problems 1 of 4 =-=[3]-=-. PSO’s main attractiveness is its simplicity and velocity allied with robustness. PSO has similar capabilities as genetic algorithms but has the advantage of simpler implementation and reduced bookke...

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...sity value I(λ ) estimated via histogram for each bin k, and m is the number of bins used. 2) Euclidean Distance: The Euclidean distance measures the resemblance of pairs of points in a feature space =-=[8]-=-. The distance d between two points x1 and x2 can be defined as n ∑ i= 1 d( x , x ) = ( x − x ) , (7) 1 2 1 where the points are measured on n axes or features. The next two metrics are texture featur...

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... or classification. In the remote sensing field, several approaches of feature extraction of hyperspectral data have been developed but mainly aimed at improving or preserving classification accuracy =-=[2]-=-. Particle swarm optimization (PSO) is a very promising evolutionary computation technique that had been developed recently for solving nonlinear optimization problems 1 of 4 [3]. PSO’s main attractiv...

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...t optimization problems, or problems that can be converted to optimization problems. PSO has been successfully applied to feature selection for quantitative structureactivity relationship correlation =-=[4]-=-. Nonetheless, the problem to define the target function to be optimized is highly dependant on the application at hand. In this paper, we present a new approach for feature extraction of hyperspectra...

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...tive measure of the extracted band’s information content. During the search procedure, the criteria are maximized. 1) Information Entropy: The entropy estimates the amount of information in the image =-=[7]-=-. The information entropy measure S, of an image λ is defined as m ∑ k = 1 S(λ ) = − p( I ) ln p( I ) , (6) k where p is the probability distribution function of the image’s intensity value I(λ ) esti...

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...cessfully demonstrated the possibility of exploring a spectral interval of less blood optical absorbance in the nearinfrared region of the spectrum to generate visualizations under the layer of blood =-=[1]-=-. The final goal is to obtain a visualization of blood-covered areas that cannot be seen in the visible region. Hyperspectral imaging systems are able to acquire several hundreds of spectral informati...