An Improved Multiobjective Discrete Particle Swarm Optimization for Hyperspectral Endmember Extraction
Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a multiobjective optimization perspective, this task is extremely challenging because objectives often conflict with each other. Currently, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is appl...
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| Published in: | IEEE transactions on geoscience and remote sensing Vol. 57; no. 10; pp. 7872 - 7882 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a multiobjective optimization perspective, this task is extremely challenging because objectives often conflict with each other. Currently, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is applied to handle the multiobjective optimization EE problem such as the root-mean-square error (RMSE) and the volume maximization (VM). However, in MODPSO, the minimization of RMSE by unconstrained least squares (Ucls) may lack accuracy, the update of velocity by the predefined random selection probability p can also affect the exploration and exploitation, and it may lose good solution in the process of the update of particles when the particles are randomly chosen in the nondominated relationship. To address these issues, we present an improved MODPSO (IMODPSO) for hyperspectral EE. IMODPSO employs nonnegative constrained least squares (Ncls) to enhance the accuracy of RMSE. Moreover, IMODPSO eliminates the effects of probability p and combines the restart mechanism to achieve a balance of the exploration and exploitation. In addition, IMODPSO utilizes the archive strategy to reserve good nondominated particles to strengthen the population diversity. The experiments have been conducted on three real hyperspectral images and the results have demonstrated that IMODPSO obtains best performances for EE. |
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| AbstractList | Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a multiobjective optimization perspective, this task is extremely challenging because objectives often conflict with each other. Currently, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is applied to handle the multiobjective optimization EE problem such as the root-mean-square error (RMSE) and the volume maximization (VM). However, in MODPSO, the minimization of RMSE by unconstrained least squares (Ucls) may lack accuracy, the update of velocity by the predefined random selection probability [Formula Omitted] can also affect the exploration and exploitation, and it may lose good solution in the process of the update of particles when the particles are randomly chosen in the nondominated relationship. To address these issues, we present an improved MODPSO (IMODPSO) for hyperspectral EE. IMODPSO employs nonnegative constrained least squares (Ncls) to enhance the accuracy of RMSE. Moreover, IMODPSO eliminates the effects of probability [Formula Omitted] and combines the restart mechanism to achieve a balance of the exploration and exploitation. In addition, IMODPSO utilizes the archive strategy to reserve good nondominated particles to strengthen the population diversity. The experiments have been conducted on three real hyperspectral images and the results have demonstrated that IMODPSO obtains best performances for EE. Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a multiobjective optimization perspective, this task is extremely challenging because objectives often conflict with each other. Currently, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is applied to handle the multiobjective optimization EE problem such as the root-mean-square error (RMSE) and the volume maximization (VM). However, in MODPSO, the minimization of RMSE by unconstrained least squares (Ucls) may lack accuracy, the update of velocity by the predefined random selection probability p can also affect the exploration and exploitation, and it may lose good solution in the process of the update of particles when the particles are randomly chosen in the nondominated relationship. To address these issues, we present an improved MODPSO (IMODPSO) for hyperspectral EE. IMODPSO employs nonnegative constrained least squares (Ncls) to enhance the accuracy of RMSE. Moreover, IMODPSO eliminates the effects of probability p and combines the restart mechanism to achieve a balance of the exploration and exploitation. In addition, IMODPSO utilizes the archive strategy to reserve good nondominated particles to strengthen the population diversity. The experiments have been conducted on three real hyperspectral images and the results have demonstrated that IMODPSO obtains best performances for EE. |
| Author | Zhang, Liangpei Du, Bo Tong, Lyuyang Liu, Rong |
| Author_xml | – sequence: 1 givenname: Lyuyang surname: Tong fullname: Tong, Lyuyang email: lyuyangtong@outlook.com organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 2 givenname: Bo orcidid: 0000-0002-0059-8458 surname: Du fullname: Du, Bo email: remoteking@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 3 givenname: Rong surname: Liu fullname: Liu, Rong email: rong.liu@dlr.de organization: Germany Aerospace Center (DLR), Weßling, Germany – sequence: 4 givenname: Liangpei orcidid: 0000-0001-6890-3650 surname: Zhang fullname: Zhang, Liangpei email: zlp62@whu.edu.cn organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China |
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| References | ref35 ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 chen (ref41) 2010 ref10 ref2 winter (ref16) 1999; 3753 ref1 ref39 ref19 ref18 boardman (ref17) 1995; 1 ref46 ref24 ref23 ref26 ref47 ref25 ref20 ref42 ref22 ref44 ref21 ref43 zhu (ref45) 2014 ref28 ref27 ref29 ref8 ref7 trivedi (ref32) 2017; 21 ref9 ref4 ref3 ref6 zitzler (ref38) 2001; 103 ref5 ref40 |
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| SubjectTerms | Accuracy Algorithms Discrete particle swarm optimization (DPSO) endmember extraction (EE) Exploitation Exploration Hyperspectral imaging Indexes Least squares Linear programming Minimization multiobjective optimization Multiple objective analysis Optimization Particle swarm optimization Probability theory Root-mean-square errors |
| Title | An Improved Multiobjective Discrete Particle Swarm Optimization for Hyperspectral Endmember Extraction |
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