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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 57; no. 10; pp. 7872 - 7882
Main Authors: Tong, Lyuyang, Du, Bo, Liu, Rong, Zhang, Liangpei
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary: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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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2019.2917001