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Piecewise Convex Mulutiple-model Hyperspectral Imagery ENDmember\|extraction based on Discrete Particle Swarm Optimization |
Liu Ailin1,2,Guo Baoping1,Li Yanshan3 |
(1.College of Optoelectronic and Engineering,Key Laboratory of Optoelectronic Devices
and Systems fo Ministry of Education, Shenzhen University,Shenzhen 518060,China;
2.College of Electronic and Information,Hunan Tecnologyand Engineering University,Yongzhou 425100,China;
3.College fo information and Engineering,Shenzhen University,Shenzhen 518060,China) |
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Abstract Piecewise COnvex Multiple\|Model ENDmember(PCOMMEND) spectral unmixing can well solve unmixing of the nonconvex hyperspectral data,which improves the calculation accuracy of the standard linear mixed model based on the convex geometry model.the number of piecewise convex is not sure in the practical application,which limits the calculation ccuracy of unmixing and the wrong endmembers will sometimes extracted,in view of the situation,the Discrete Particle Swarm Optimization(D\|PSO)is proposed to unmix the piecewise convex mulutiple\|model hyperspectral imagery,D\|PSO is the intelligent algorithm of random search,and is able to find the global optimal solution of convex function,which reduce the unmixing error caused by the uncertainty number of the convex section,experiments on the simulative data and real data has indicate D\|PSO improves the accuracy of the extracting endmember and estimating the proportion.
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Received: 27 November 2017
Published: 15 May 2018
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